Energy expenditure

ABSTRACT

Aspects relate to calculating energy expenditure values from an apparatus configured to be worn on an appendage of a user. Steps counts may be quantified, such as by detecting arm swings peaks and bounce peaks in motion data. A search range of acceleration frequencies related to an expected activity may be established. Frequencies of acceleration data within a search range may be analyzed to identify one or more peaks, such as a bounce peak and an arm swing peak. Novel systems and methods may determine whether to utilize the arm swing data, bounce data, and/or other data or portions of data to quantify steps. The number of peaks (and types of peaks) may be used to choose a step frequency and step magnitude. At least a portion of the motion data may be classified into an activity category based upon the quantification of steps.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/355,243, filed Nov. 18, 2016, issued as U.S. Pat. No. 9,747,411,which is a continuation of U.S. patent application Ser. No. 13/744,103,filed Jan. 17, 2013, issued as U.S. Pat. No. 9,529,966, which claims thebenefit of, and priority to, U.S. Provisional Patent Application No.61/588,647 filed Jan. 19, 2012. The content of each of theseapplications is expressly incorporated herein by reference in itsentirety for any and all non-limiting purposes.

BACKGROUND

While most people appreciate the importance of physical fitness, manyhave difficulty finding the motivation required to maintain a regularexercise program. Some people find it particularly difficult to maintainan exercise regimen that involves continuously repetitive motions, suchas running, walking and bicycling.

Additionally, individuals may view exercise as work or a chore and thus,separate it from enjoyable aspects of their daily lives. Often, thisclear separation between athletic activity and other activities reducesthe amount of motivation that an individual might have towardexercising. Further, athletic activity services and systems directedtoward encouraging individuals to engage in athletic activities mightalso be too focused on one or more particular activities while anindividual's interest are ignored. This may further decrease a user'sinterest in participating in athletic activities or using athleticactivity services and systems.

Many existing services and devices fail to provide accurate assessmentof the user's energy expenditure, such as caloric burn, during physicalactivity. Therefore, users are unaware of the benefits that certainactivities, which may include daily routines that are often not thoughtof as being a “workout”, are to their health. Existing devices forallowing users to monitor their energy expenditure often suffer from oneor more deficiencies, including: cumbersome collection systems,inaccurate measurements that are beyond an acceptable threshold,unacceptable latency in reporting the values, erroneous classificationof activities based upon detected motions of the user, failure toaccount for deviations between different users (for example, properclassification for individuals who do not “bounce” during walking and/orrunning to the same extent as an “average” individual), improperlyincluding repetitive behavior as being classified as a specificactivity, such as for example, running and/or walking, relatively highpower consumption, and/or a combination of these or other deficiencies.

Therefore, improved systems and methods to address at least one or moreof these shortcomings in the art are desired.

BRIEF SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of this disclosure relate to calculating energy expenditurevalues. One or more devices may use an accelerometer and/or othersensors to monitor physical activities of a user. In one embodiment, anapparatus is configured to be worn on an appendage of a user and may beused to collect and process motion data. The apparatus may include aprocessor, at least one sensor configured to capture motion data of theuser, and a memory comprising computer-executable instructions that whenexecuted by the processor, collects and analyzes the motion data. Themotion data may be utilized to determine an energy expenditure value.The apparatus may be configured to capture motion data of the user withthe sensor while being worn on an appendage of the user. It may beconfigured to be worn on the arm, such as but not limited to beinglocated by a wrist, of the user. Certain implementations may be entirelyperformed on a single apparatus.

In certain embodiments, the only sensor data utilized to collect themotion data is collected (or derived) from the apparatus worn by theuser. In further embodiments, at least one of the following is performedentirely on the device worn by the user: quantification of steps,determining which data used to quantify and/or detect steps, organizingthe data into activity categories, and/or determining an energyexpenditure value. In certain embodiments, the apparatus alreadycontains information, such as a metabolic equivalence value or data orinformation utilized in the calculations on a computer-readable mediumlocated on the apparatus. Thus, no external information is requiredduring the calculations.

Certain implementations may quantify steps taken by the user based uponthe motion data, such as by detecting arm swings peaks and bounce peaksin the motion data. The quantification may be done based entirely upondata collected from a single device worn on the user's arm, such as forexample, proximate to the wrist. In one embodiment, motion data isobtained from an accelerometer. Accelerometer magnitude vectors may beobtained for a time frame and values, such as an average value frommagnitude vectors for the time frame may be calculated. The averagevalue (or any other value) may be utilized to determine whethermagnitude vectors for the time frame meet an acceleration threshold toqualify for use in calculating step counts for the respective timeframe. Acceleration data meeting a threshold may be placed in ananalysis buffer. A search range of acceleration frequencies related toan expected activity may be established. Frequencies of the accelerationdata within the search range may be analyzed in certain implementationsto identify one or more peaks, such as a bounce peak and an arm swingpeak. In one embodiment, a first frequency peak may be identified as anarm swing peak if it is within an estimated arm swing range and furthermeets an arm swing peak threshold. Similarly, a second frequency peakmay be determined to be a bounce peak if it is within an estimatedbounce range and further meets a bounce peak threshold.

Novel systems and methods may determine whether to utilize the arm swingdata, bounce data, and/or other data or portions of data to quantifysteps. The number of peaks, such as arm swing peaks and/or bounce peaksmay be used to determine which data to utilize. In one embodiment,systems and methods may use the number of peaks (and types of peaks) tochoose a step frequency and step magnitude for quantifying steps. Instill further embodiments, at least a portion of the motion data may beclassified into an activity category based upon the quantification ofsteps.

In one embodiment, the sensor signals (such as accelerometerfrequencies) and the calculations based upon sensor signals (e.g., aquantity of steps) may be utilized in the classification of an activitycategory, such as either walking or running, for example. In certainembodiments, if data cannot be categorized as being within a firstcategory (e.g., walking) or group of categories (e.g., walking andrunning), a first method may analyze collected data. For example, in oneembodiment, if detected parameters cannot be classified, then aEuclidean norm equation may be utilized for further analysis. In oneembodiment, an average magnitude vector norm (square root of the sum ofthe squares) of obtained values may be utilized. In yet anotherembodiment, a different method may analyze at least a portion of thedata following classification within a first category or groups ofcategories. In one embodiment, a step algorithm, such as those disclosedherein, may be utilized. Classified and unclassified data may beutilized to calculate an energy expenditure value.

A memory may include instructions that when executed by the processor ofthe apparatus, combine the energy expenditure value for the first timeperiod with an energy expenditure value from a second time period todetermine an accumulated energy expenditure value. An apparatus mayinclude a display configured to be observable by the user while theapparatus is being worn by that user. The device may be configured todisplay the accumulated energy expenditure value on the display. Thedisplaying of energy expenditure values on a device may be responsive toreceiving a user input from a user input device located on the device.The display may include a length of light-emitting structures, such asLEDs that are configured to provide an indication of the energyexpenditure. In one embodiment, the displayed expenditure may be inrelation to a goal, such as a goal set by the user.

In some embodiments, the present invention can be partially or whollyimplemented on a computer-readable medium, for example, by storingcomputer-executable instructions or modules, or by utilizingcomputer-readable data structures.

Of course, the methods and systems of the above-referenced embodimentsmay also include other additional elements, steps, computer-executableinstructions, or computer-readable data structures.

The details of these and other embodiments of the present invention areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIGS. 1A-B illustrate an example of a system that may be used to collectand analyze motion data, wherein FIG. 1A illustrates an example networkconfigured to collect and analyze athletic activity, and FIG. 1Billustrates an example computing device in accordance with exampleembodiments;

FIGS. 2A and 2B illustrate example sensor assemblies that may be worn bya user in accordance with example embodiments;

FIG. 3 shows an example flowchart that may be utilized to quantifyenergy expenditure values in accordance with one embodiment;

FIGS. 4A and 4B show an example flowchart that may be utilized toquantify steps in accordance with one embodiment. Specifically, FIG. 4Ais a flowchart that may be used to collect and analyze motion data inaccordance with one embodiment and FIG. 4B is a flowchart that may beused to identify data ranges for detecting steps or other physicalactivities of a user in accordance with one embodiment;

FIG. 5 shows an example flowchart that may estimate frequency and set upa frequency search range in accordance with one embodiment;

FIG. 6 shows a graph illustrating an example search range of motion datain accordance with certain embodiments;

FIGS. 7A and 7B show a graph illustrating a sample FFT output.Specifically, FIG. 7A shows a graph plotting FFT power against frequencydata that includes data within an arm swing range and data within abounce range; and FIG. 7B shows the same graph with a threshold utilizedto determine if peaks within the bounce range meet a criterion;

FIGS. 8A and 8B show example flowcharts that may be implemented indeterminations of whether to utilize arm swing frequency, bouncefrequency and/or other frequencies in accordance with one embodiment;

FIG. 9 shows an example flowchart that may be implemented to classifyactivity and determine speed in accordance with one embodiment;

FIG. 10 shows an example flowchart that may be implemented to determineenergy expenditure values in accordance with one embodiment;

FIG. 11 graphically represents an example accumulation of energyexpenditure values based on illustrative frequency data that may beimplemented in some embodiments; and

FIG. 12 shows a flowchart of an embodiment of measuring activity of auser that may be implemented in conjunction with, or independently of,other embodiments described herein.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration various embodiments in which thedisclosure may be practiced. It is to be understood that otherembodiments may be utilized and structural and functional modificationsmay be made without departing from the scope and spirit of the presentdisclosure. Further, headings within this disclosure should not beconsidered as limiting aspects of the disclosure. Those skilled in theart with the benefit of this disclosure will appreciate that the exampleembodiments are not limited to the example headings.

I. Example Personal Training System

A. Illustrative Computing Devices

FIG. 1A illustrates an example of a personal training system 100 inaccordance with example embodiments. Example system 100 may include oneor more electronic devices, such as computer 102. Computer 102 maycomprise a mobile terminal, such as a telephone, music player, tablet,netbook or any portable device. In other embodiments, computer 102 maycomprise a set-top box (STB), desktop computer, digital videorecorder(s) (DVR), computer server(s), and/or any other desiredcomputing device. In certain configurations, computer 102 may comprise agaming console, such as for example, a Microsoft® XBOX, Sony®PlayStation, and/or a Nintendo® Wii gaming consoles. Those skilled inthe art will appreciate that these are merely example consoles fordescriptive purposes and this disclosure is not limited to any consoleor device.

Turning briefly to FIG. 1B, computer 102 may include computing unit 104,which may comprise at least one processing unit 106. Processing unit 106may be any type of processing device for executing softwareinstructions, such as for example, a microprocessor device. Computer 102may include a variety of non-transitory computer readable media, such asmemory 108. Memory 108 may include, but is not limited to, random accessmemory (RAM) such as RAM 110, and/or read only memory (ROM), such as ROM112. Memory 108 may include any of: electronically erasable programmableread only memory (EEPROM), flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic storage devices, or any other medium that can be used to storethe desired information and that can be accessed by computer 102.

The processing unit 106 and the system memory 108 may be connected,either directly or indirectly, through a bus 114 or alternatecommunication structure to one or more peripheral devices. For example,the processing unit 106 or the system memory 108 may be directly orindirectly connected to additional memory storage, such as a hard diskdrive 116, a removable magnetic disk drive, an optical disk drive 118,and a flash memory card. The processing unit 106 and the system memory108 also may be directly or indirectly connected to one or more inputdevices 120 and one or more output devices 122. The output devices 122may include, for example, a display device 136, television, printer,stereo, or speakers. In some embodiments one or more display devices maybe incorporated into eyewear. The display devices incorporated intoeyewear may provide feedback to users. Eyewear incorporating one or moredisplay devices also provides for a portable display system. The inputdevices 120 may include, for example, a keyboard, touch screen, a remotecontrol pad, a pointing device (such as a mouse, touchpad, stylus,trackball, or joystick), a scanner, a camera or a microphone. In thisregard, input devices 120 may comprise one or more sensors configured tosense, detect, and/or measure athletic movement from a user, such asuser 124, shown in FIG. 1A.

Looking again to FIG. 1A, image-capturing device 126 and/or sensor 128may be utilized in detecting and/or measuring athletic movements of user124. In one embodiment, data obtained from image-capturing device 126 orsensor 128 may directly detect athletic movements, such that the dataobtained from image-capturing device 126 or sensor 128 is directlycorrelated to a motion parameter. Yet, in other embodiments, data fromimage-capturing device 126 and/or sensor 128 may be utilized incombination, either with each other or with other sensors to detectand/or measure movements. Thus, certain measurements may be determinedfrom combining data obtained from two or more devices. Image-capturingdevice 126 and/or sensor 128 may include or be operatively connected toone or more sensors, including but not limited to: an accelerometer, agyroscope, a location-determining device (e.g., GPS), light sensor,temperature sensor (including ambient temperature and/or bodytemperature), heart rate monitor, image-capturing sensor, moisturesensor and/or combinations thereof. Example uses of illustrative sensors126, 128 are provided below in Section I.C, entitled “IllustrativeSensors.” Computer 102 may also use touch screens or image capturingdevice to determine where a user is pointing to make selections from agraphical user interface. One or more embodiments may utilize one ormore wired and/or wireless technologies, alone or in combination,wherein examples of wireless technologies include Bluetooth®technologies, Bluetooth® low energy technologies, and/or ANTtechnologies.

B. Illustrative Network

Computer 102, computing unit 104, and/or any other electronic devicesmay be directly or indirectly connected to one or more networkinterfaces, such as example interface 130 (shown in FIG. 1B) forcommunicating with a network, such as network 132. In the example ofFIG. 1B, network interface 130, may comprise a network adapter ornetwork interface card (NIC) configured to translate data and controlsignals from the computing unit 104 into network messages according toone or more communication protocols, such as the Transmission ControlProtocol (TCP), the Internet Protocol (IP), and the User DatagramProtocol (UDP). These protocols are well known in the art, and thus willnot be discussed here in more detail. An interface 130 may employ anysuitable connection agent for connecting to a network, including, forexample, a wireless transceiver, a power line adapter, a modem, or anEthernet connection. Network 132, however, may be any one or moreinformation distribution network(s), of any type(s) or topology(s),alone or in combination(s), such as internet(s), intranet(s), cloud(s),LAN(s). Network 132 may be any one or more of cable, fiber, satellite,telephone, cellular, wireless, etc. Networks are well known in the art,and thus will not be discussed here in more detail. Network 132 may bevariously configured such as having one or more wired or wirelesscommunication channels to connect one or more locations (e.g., schools,businesses, homes, consumer dwellings, network resources, etc.), to oneor more remote servers 134, or to other computers, such as similar oridentical to computer 102. Indeed, system 100 may include more than oneinstance of each component (e.g., more than one computer 102, more thanone display 136, etc.).

Regardless of whether computer 102 or other electronic device withinnetwork 132 is portable or at a fixed location, it should be appreciatedthat, in addition to the input, output and storage peripheral devicesspecifically listed above, the computing device may be connected, suchas either directly, or through network 132 to a variety of otherperipheral devices, including some that may perform input, output andstorage functions, or some combination thereof. In certain embodiments,a single device may integrate one or more components shown in FIG. 1A.For example, a single device may include computer 102, image-capturingdevice 126, sensor 128, display 136 and/or additional components. In oneembodiment, sensor device 138 may comprise a mobile terminal having adisplay 136, image-capturing device 126, and one or more sensors 128.Yet, in another embodiment, image-capturing device 126, and/or sensor128 may be peripherals configured to be operatively connected to a mediadevice, including for example, a gaming or media system. Thus, it goesfrom the foregoing that this disclosure is not limited to stationarysystems and methods. Rather, certain embodiments may be carried out by auser 124 in almost any location.

C. Illustrative Sensors

Computer 102 and/or other devices may comprise one or more sensors 126,128 configured to detect and/or monitor at least one fitness parameterof a user 124. Sensors 126 and/or 128 may include, but are not limitedto: an accelerometer, a gyroscope, a location-determining device (e.g.,GPS), light sensor, temperature sensor (including ambient temperatureand/or body temperature), sleep pattern sensors, heart rate monitor,image-capturing sensor, moisture sensor and/or combinations thereof.Network 132 and/or computer 102 may be in communication with one or moreelectronic devices of system 100, including for example, display 136, animage capturing device 126 (e.g., one or more video cameras), and sensor128, which may be an infrared (IR) device. In one embodiment sensor 128may comprise an IR transceiver. For example, sensors 126, and/or 128 maytransmit waveforms into the environment, including towards the directionof user 124 and receive a “reflection” or otherwise detect alterationsof those released waveforms. In yet another embodiment, image-capturingdevice 126 and/or sensor 128 may be configured to transmit and/orreceive other wireless signals, such as radar, sonar, and/or audibleinformation. Those skilled in the art will readily appreciate thatsignals corresponding to a multitude of different data spectrums may beutilized in accordance with various embodiments. In this regard, sensors126 and/or 128 may detect waveforms emitted from external sources (e.g.,not system 100). For example, sensors 126 and/or 128 may detect heatbeing emitted from user 124 and/or the surrounding environment. Thus,image-capturing device 126 and/or sensor 128 may comprise one or morethermal imaging devices. In one embodiment, image-capturing device 126and/or sensor 128 may comprise an IR device configured to perform rangephenomenology. As a non-limited example, image-capturing devicesconfigured to perform range phenomenology are commercially availablefrom Flir Systems, Inc. of Portland, Oreg. Although image capturingdevice 126 and sensor 128 and display 136 are shown in direct(wirelessly or wired) communication with computer 102, those skilled inthe art will appreciate that any may directly communicate (wirelessly orwired) with network 132.

1. Multi-Purpose Electronic Devices

User 124 may possess, carry, and/or wear any number of electronicdevices, including sensory devices 138, 140, 142, and/or 144. In certainembodiments, one or more devices 138, 140, 142, 144 may not be speciallymanufactured for fitness or athletic purposes. Indeed, aspects of thisdisclosure relate to utilizing data from a plurality of devices, some ofwhich are not fitness devices, to collect, detect, and/or measureathletic data. In one embodiment, device 138 may comprise a portableelectronic device, such as a telephone or digital music player,including an IPOD®, IPAD®, or iPhone®, brand devices available fromApple, Inc. of Cupertino, Calif. or Zune® or Microsoft® Windows devicesavailable from Microsoft of Redmond, Wash. As known in the art, digitalmedia players can serve as both an output device for a computer (e.g.,outputting music from a sound file or pictures from an image file) and astorage device. In one embodiment, device 138 may be computer 102, yetin other embodiments, computer 102 may be entirely distinct from device138. Regardless of whether device 138 is configured to provide certainoutput, it may serve as an input device for receiving sensoryinformation. Devices 138, 140, 142, and/or 144 may include one or moresensors, including but not limited to: an accelerometer, a gyroscope, alocation-determining device (e.g., GPS), light sensor, temperaturesensor (including ambient temperature and/or body temperature), heartrate monitor, image-capturing sensor, moisture sensor and/orcombinations thereof. In certain embodiments, sensors may be passive,such as reflective materials that may be detected by image-capturingdevice 126 and/or sensor 128 (among others). In certain embodiments,sensors 144 may be integrated into apparel, such as athletic clothing.For instance, the user 124 may wear one or more on-body sensors 144 a-b.Sensors 144 may be incorporated into the clothing of user 124 and/orplaced at any desired location of the body of user 124. Sensors 144 maycommunicate (e.g., wirelessly) with computer 102, sensors 128, 138, 140,and 142, and/or camera 126. Examples of interactive gaming apparel aredescribed in U.S. patent application Ser. No. 10/286,396, filed Oct. 30,2002, and published as U.S. Pat. Pub, No. 2004/0087366, the contents ofwhich are incorporated herein by reference in its entirety for any andall non-limiting purposes. In certain embodiments, passive sensingsurfaces may reflect waveforms, such as infrared light, emitted byimage-capturing device 126 and/or sensor 128. In one embodiment, passivesensors located on user's 124 apparel may comprise generally sphericalstructures made of glass or other transparent or translucent surfaceswhich may reflect waveforms. Different classes of apparel may beutilized in which a given class of apparel has specific sensorsconfigured to be located proximate to a specific portion of the user's124 body when properly worn. For example, golf apparel may include oneor more sensors positioned on the apparel in a first configuration andyet soccer apparel may include one or more sensors positioned on apparelin a second configuration.

Devices 138-144 may communicate with each other, either directly orthrough a network, such as network 132. Communication between one ormore of devices 138-144 may take place via computer 102. For example,two or more of devices 138-144 may be peripherals operatively connectedto bus 114 of computer 102. In yet another embodiment, a first device,such as device 138 may communicate with a first computer, such ascomputer 102 as well as another device, such as device 142; however,device 142 may not be configured to connect to computer 102 but maycommunicate with device 138. Those skilled in the art will appreciatethat other configurations are possible.

Some implementations of the example embodiments may alternately oradditionally employ computing devices that are intended to be capable ofa wide variety of functions, such as a desktop or laptop personalcomputer. These computing devices may have any combination of peripheraldevices or additional components as desired. Also, the components shownin FIG. 1B may be included in the server 134, other computers,apparatuses, etc.

2. Illustrative Apparel/Accessory Sensors

In certain embodiments, sensory devices 138, 140, 142 and/or 144 may beformed within or otherwise associated with user's 124 clothing oraccessories, including a watch, armband, wristband, necklace, shirt,shoe, or the like. Examples of shoe-mounted and wrist-worn devices(devices 140 and 142, respectively) are described immediately below,however, these are merely example embodiments and this disclosure shouldnot be limited to such.

i. Shoe-mounted Device

In certain embodiments, sensory device 140 may comprise footwear whichmay include one or more sensors, including but not limited to: anaccelerometer, location-sensing components, such as GPS, and/or a forcesensor system. FIG. 2A illustrates one example embodiment of a sensorsystem 202. In certain embodiments, system 202 may include a sensorassembly 204. Assembly 204 may comprise one or more sensors, such as forexample, an accelerometer, location-determining components, and/or forcesensors. In the illustrated embodiment, assembly 204 incorporates aplurality of sensors, which may include force-sensitive resistor (FSR)sensors 206. In yet other embodiments, other sensor(s) may be utilized.Port 208 may be positioned within a sole structure 209 of a shoe. Port208 may optionally be provided to be in communication with an electronicmodule 210 (which may be in a housing 211) and a plurality of leads 212connecting the FSR sensors 206 to the port 208. Module 210 may becontained within a well or cavity in a sole structure of a shoe. Theport 208 and the module 210 include complementary interfaces 214, 216for connection and communication.

In certain embodiments, at least one force-sensitive resistor 206 shownin FIG. 2A may contain first and second electrodes or electricalcontacts 218, 220 and a force-sensitive resistive material 222 disposedbetween the electrodes 218, 220 to electrically connect the electrodes218, 220 together. When pressure is applied to the force-sensitivematerial 222, the resistivity and/or conductivity of the force-sensitivematerial 222 changes, which changes the electrical potential between theelectrodes 218, 220. The change in resistance can be detected by thesensor system 202 to detect the force applied on the sensor 216. Theforce-sensitive resistive material 222 may change its resistance underpressure in a variety of ways. For example, the force-sensitive material222 may have an internal resistance that decreases when the material iscompressed, similar to the quantum tunneling composites described ingreater detail below. Further compression of this material may furtherdecrease the resistance, allowing quantitative measurements, as well asbinary (on/off) measurements. In some circumstances, this type offorce-sensitive resistive behavior may be described as “volume-basedresistance,” and materials exhibiting this behavior may be referred toas “smart materials.” As another example, the material 222 may changethe resistance by changing the degree of surface-to-surface contact.This can be achieved in several ways, such as by using microprojectionson the surface that raise the surface resistance in an uncompressedcondition, where the surface resistance decreases when themicroprojections are compressed, or by using a flexible electrode thatcan be deformed to create increased surface-to-surface contact withanother electrode. This surface resistance may be the resistance betweenthe material 222 and the electrode 218, 220 222 and/or the surfaceresistance between a conducting layer (e.g., carbon/graphite) and aforce-sensitive layer (e.g., a semiconductor) of a multi-layer material222. The greater the compression, the greater the surface-to-surfacecontact, resulting in lower resistance and enabling quantitativemeasurement. In some circumstances, this type of force-sensitiveresistive behavior may be described as “contact-based resistance.” It isunderstood that the force-sensitive resistive material 222, as definedherein, may be or include a doped or non-doped semiconducting material.

The electrodes 218, 220 of the FSR sensor 216 can be formed of anyconductive material, including metals, carbon/graphite fibers orcomposites, other conductive composites, conductive polymers or polymerscontaining a conductive material, conductive ceramics, dopedsemiconductors, or any other conductive material. The leads 212 can beconnected to the electrodes 218, 220 by any suitable method, includingwelding, soldering, brazing, adhesively joining, fasteners, or any otherintegral or non-integral joining method. Alternately, the electrode 218,220 and associated lead 212 may be formed of a single piece of the samematerial.

ii. Wrist-worn Device

As shown in FIG. 2B, device 226 (which may resemble or be sensory device142 shown in FIG. 1A) may be configured to be worn by user 124, such asaround a wrist, arm, ankle or the like. Device 226 may monitor athleticmovements of a user, including all-day activity of user 124. In thisregard, device assembly 226 may detect athletic movement during user's124 interactions with computer 102 and/or operate independently ofcomputer 102. For example, in one embodiment, device 226 may be an-allday activity monitor that measures activity regardless of the user'sproximity or interactions with computer 102. Device 226 may communicatedirectly with network 132 and/or other devices, such as devices 138and/or 140. In other embodiments, athletic data obtained from device 226may be utilized in determinations conducted by computer 102, such asdeterminations relating to which exercise programs are presented to user124. In one embodiment, device 226 may also wirelessly interact with amobile device, such as device 138 associated with user 124 or a remotewebsite such as a site dedicated to fitness or health related subjectmatter. At some predetermined time, the user may wish to transfer datafrom the device 226 to another location.

As shown in FIG. 2B, device 226 may include an input mechanism, such asa depressible input button 228 assist in operation of the device 226.The input button 228 may be operably connected to a controller 230and/or any other electronic components, such as one or more of theelements discussed in relation to computer 102 shown in FIG. 1B.Controller 230 may be embedded or otherwise part of housing 232. Housing232 may be formed of one or more materials, including elastomericcomponents and comprise one or more displays, such as display 234. Thedisplay may be considered an illuminable portion of the device 226. Thedisplay 234 may include a series of individual lighting elements orlight members such as LED lights 234 in an exemplary embodiment. The LEDlights may be formed in an array and operably connected to thecontroller 230. Device 226 may include an indicator system 236, whichmay also be considered a portion or component of the overall display234. It is understood that the indicator system 236 can operate andilluminate in conjunction with the display 234 (which may have pixelmember 235) or completely separate from the display 234. The indicatorsystem 236 may also include a plurality of additional lighting elementsor light members 238, which may also take the form of LED lights in anexemplary embodiment. In certain embodiments, indicator system mayprovide a visual indication of goals, such as by illuminating a portionof lighting members 238 to represent accomplishment towards one or moregoals.

A fastening mechanism 240 can be unlatched wherein the device 226 can bepositioned around a wrist of the user 124 and the fastening mechanism240 can be subsequently placed in a latched position. The user can wearthe device 226 at all times if desired. In one embodiment, fasteningmechanism 240 may comprise an interface, including but not limited to aUSB port, for operative interaction with computer 102 and/or devices138, 140.

In certain embodiments, device 226 may comprise a sensor assembly (notshown in FIG. 2B). The sensor assembly may comprise a plurality ofdifferent sensors. In an example embodiment, the sensor assembly maycomprise or permit operative connection to an accelerometer (includingin the form of a multi-axis accelerometer), heart rate sensor,location-determining sensor, such as a GPS sensor, and/or other sensors.Detected movements or parameters from device's 142 sensor(s), mayinclude (or be used to form) a variety of different parameters, metricsor physiological characteristics including but not limited to speed,distance, steps taken, calories, heart rate, sweat detection, effort,oxygen consumed, and/or oxygen kinetics. Such parameters may also beexpressed in terms of activity points or currency earned by the userbased on the activity of the user.

II. Energy Expenditure

Certain aspects of this disclosure relate to determining energyexpenditure, such as with one or more of the sensors of system 100. Inone embodiment, sensors solely located on a device configured to be wornby a user, such as a wrist-worn device, may be utilized to detectedmotion parameters. Data from sensors on such as device may be usedwithout the assistance of other sensors in one or more determinationsrelating to classifying activity and/or determine energy expenditure.The activity may include athletic and/or other physical activity of user124. FIG. 3 is a flowchart 300 showing an illustrative process that maybe utilized to classify activity and/or calculate energy expenditurevalues of an individual in accordance with one embodiment. FIG. 3 isprovided as an overview of exemplary embodiments that may comprise aplurality of sub-elements. In this regard, the remaining figures (andrelated disclosure) following FIG. 3 may optionally be used inconjunction with FIG. 3 and/or each other to provide a full system thatobtains sensor data and provides energy expenditure values. Inaccordance with other embodiments, one or more different systems andmethods discussed below may be used alone or in combination with only aportion of other disclosed systems and methods to provide one or moreof: step counts, activity classifications, and energy expenditures,among others. Various embodiments of step quantification systems andmethods may relate to a low power, high fidelity, integer-based stepcounter using a multi-tier technique. These and other embodiments aredescribed below.

In accordance with a first embodiment, a plurality of samples from oneor more sensors (e.g., sensors 126, 128, and/or 138-142) may be obtainedduring a first time period (see, e.g., block 302). In certainconfigurations, at least one sensor (e.g. sensor 142) may comprise anaccelerometer. The accelerometer may be a multi-axis accelerometer. Inanother embodiment, however, a plurality of accelerometers may beutilized. Other non-accelerometer based sensors are also within thescope of this disclosure, either in combination with an accelerometer orindividually. Indeed, any sensor(s) configurable to detect or measureathletic movement and/or physiologic properties are within the scope ofthis disclosure. In this regard, data may be obtained and/or derivedfrom a plurality of sensors, including for example, location sensors(e.g., GPS), heart rate sensors, force sensors, gyroscope, etc. In oneembodiment, various systems and methods are implemented, at leastpartially, on a portable device. In certain embodiments, the portabledevice may be a wrist-worn device (see, e.g., sensor 142). In oneembodiment, sensor data from a device configured to be worn on a humanappendage (e.g., wrist, arm, neck, ankles, leg, etc.) may be utilizedwithout other sensor data. Motion data, such as measured through anaccelerometer and/or other sensors, may be loaded into a multi-segmentthreshold based acceleration buffer.

Further aspects relate to detecting and/or measuring an athleticparameter, such as for example, a quantity of steps taken by a user,such as user 124. One or more system or methods may utilize variousportions of the data (such as in an acceleration buffer comprisingaccelerometer data) to determine if detected parameters are indicativeof a specific action or activity. In one embodiment, a quantity of stepsmay be detected during a predefined period of time (See, e.g., block304). Examples of different systems and methods that may be utilized toquantify the number of steps taken by the user during a time period (oreven determine whether steps exist in the sensor data) are provided incontext of FIGS. 4-8, and will be discussed below. In one embodiment,step data and/or other motion data may be utilized in the classificationof activity, such as either walking or running, for example (see, e.g.,block 306). In certain embodiments, if data cannot be categorized asbeing within a first category (e.g., walking) or group of categories(e.g., walking and running), a first method may analyze collected data.For example, in one embodiment, if detected parameters cannot beclassified, then a Euclidean norm equation may be utilized for furtheranalysis. In one embodiment, an average magnitude vector norm (squareroot of the sum of the squares) of obtained values may be utilized. Inyet another embodiment, a different method may analyze at least aportion of the data following classification within a first category orgroups of categories. In one embodiment, a step algorithm, such as thosedisclosed herein, may be utilized. This disclosure further provides someexamples of classification processes that may be implemented (see, e.g.,FIG. 9).

Further embodiments may utilize the classified activity data and/orunclassified activity data to estimate the energy expenditure of theuser's detected motions as sensed by one or more of the sensors (e.g.,block 308). FIG. 10 provides one example that may be implemented todetermine energy expenditure. FIG. 11 graphically represents oneembodiment of accumulating energy expenditure values, which may forexample, be used to determine caloric burn in some embodiments.

Further embodiments relate to adjusting energy expenditure valuesaccording to at least one activity factor. In some embodiments there isnot a one-to-one correlation between an activity and an activity factor.The selection of an activity factor may be based on several differentvariables, such as the activity identified, steps taken, heart rate, andintensity of a workout. FIG. 12 illustrates a method for calculatingenergy expenditure points, in accordance with an embodiment of theinvention.

Aspects of various embodiments may offer one or more advantages and/orbenefits over the prior-known systems and methods. In certainembodiments, false positives are reduced or eliminated forshort-duration arm movements using a buffer filling strategy. Using aconstrained search for analysis (e.g. FFT) may assist in selecting thecorrect frequency (e.g., frequencies relating a vertical bounce ratherthan the arm swing such that the correct walking frequency is obtainedfor two feet steps). In further embodiments, the overlapping of motiondata windows may allow for improved detection of short bursts ofactivities (e.g., step activities). Finally, the frequency analysis maybe performed on one combined channel of sensors so that arm rotationdoes not throw off detection and measurement of sensor outputs.Furthermore, by combining accelerometer channels, less analysis (e.g.Fourier transform frequency analyses) may be performed. This may improvebattery life. One or more of these advantages may be realized on aportable device configured to be worn on an appendage of the user duringthe performance of the physical motions.

FIG. 4 shows flowchart 400 of an illustrative method that may beutilized to quantify performance of a specific activity such as steps,which may occur during walking, running, or any other physicalactivities of an individual. One or more processes of FIG. 4 may beimplemented as part of block 304. Alternatively, one or more portions offlowchart 400 may be conducted independently of block 302 or any otherprocess disclosed herein.

Flowchart 400 may initiate with block 402 to obtain data relating toathletic movements. The data may be calculated or otherwise obtainedfrom the sensor data of block 302. In certain embodiments, at least aportion of any quantifications or calculations may be conducted on aportable device, including a wrist-worn device (e.g., sensor 142).Further, a single device (such as device 142/226) and/or sensor (e.g.,an accelerometer) may provide data that is utilized to determinemultiple different movements. Specific embodiments relate to systems andmethods that may be used on a single portable device configured to beworn on an appendage (such as an arm or leg) comprise all the sensorsand/or other information utilized to collect and process motion data andprovide an output of the data to a user.

In one embodiment, a single multi-axis accelerometer may provide datarelating to actual steps (such as detecting bounce due to stepping) andarm swing movement of a user. In one embodiment, device/sensor 226 isconfigured to detect bounce data from stepping of the wearer as well ascollect arm swing data. In one embodiment, a single unitary device thatis configured to be worn on the wrist is enabled to collectaccelerometer data based upon the user's arm swings and bounce fromstepping. An illustrative example of detecting arm swing and bounce dataare provided below in FIG. 5.

Collecting athletic data relating to a plurality of movements, such asbounce data and arm swing data may, in certain embodiments, provide oneor more benefits not obtained in prior art systems and methods,including for example: improved accuracy, and decreased latency inreporting the values. Further benefits provided by one or moreembodiments not provided in the art include classification of activitiesthat are based upon step count (or the relevant athletic movement). Forexample, certain individuals do not “bounce” during walking and/orrunning to the same extent as an “average” individual. Further, certainembodiments may result in excluding repetitive behavior from improperlybeing classified as a specific activity, such as for example, runningand/or walking. Still yet further benefits may include improveddeterminations of intensity and/or speed and utilization of thosedeterminations in activity classification, improved power consumptions,and/or a combination of these or other improvements.

Data obtained at block 402 may be obtained from one or more sensors,including either carried or worn by the user or those fixed in specificlocations, such as within a wrist-worn device 226. In accordance with afirst embodiment, a plurality of samples from one or more sensors may beobtained during a first time period. In one embodiment, at least onesensor comprises an accelerometer. The accelerometer may be a multi-axisaccelerometer. In another embodiment, a plurality of accelerometers maybe utilized. Other non-accelerometer based sensors are also within thescope of this disclosure.

Block 402 (or 302) may be obtained at a fixed sampling rate, yet inother embodiments, a variable sampling rate may be implemented for atleast one of the sensors. In one embodiment, a 25 Hertz sampling ratemay be utilized. In one such embodiment, utilizing a 25 Hz sampling rateto obtain accelerometer data from an appendage-worn (e.g., wrist-worn)portable device may adequately obtain data, such as for example, stepcounts while obtaining acceptable battery life as compared to otherprior art methodologies. In yet another embodiment, a 50 Hz samplingrate may be utilized. These rates are merely illustrative and otherrates are within the scope of this disclosure. In certain embodiments,the first time period may be 1 second. In one embodiment, 64 samples ofdata may be obtained during the first time period. Each sample of datamay have multiple parameters, such as motion vectors for multiple axes,however, in other embodiments; each sample of data is a single value.Certain implementations may provide data comprising multiple values as asingle value. For example, data from a 3-axis accelerometer may beprovided as a single value.

The collected data may be analyzed or processed, which may occur uponcollection, at predefined intervals, upon occurrence of predefinedcriteria, at a later time, or combinations thereof. In certainimplementations, samples within the first time period may be meancentered and/scaled.

Samples (or data relating to the received samples) from the first timeperiod may be placed in a buffer (see, e.g., block 404). Those skilledin the art realize that one or more buffers may be part of any one ormore computer-readable mediums, such as computer-readable mediums 110and/or 112 within system memory 108. One or more systems or methods maybe implemented to determine whether samples from the first time periodare placed in a first buffer. One or more factors may determine whethersamples from the first time period are placed within a buffer. Forexample, accuracy and/or reliability may be considered.

In one embodiment, about 128 samples may be placed in a first buffer. Inanother embodiment, the buffer duration may differ. In certainembodiments, the buffer may be about twice (e.g., 2×) the first timeperiod. For example, if the first time period is 1 second, then thebuffer duration may be 2 seconds in certain embodiments. The buffer maybe a specific time duration (e.g., 2 seconds) regardless of the durationof the first time period. The buffer duration may depend on one or morefactors, including for example but not limited to: battery life, desiredenergy consumption, sampling rate, samples obtained, a desired wait timebefore calculation procedures and/or combinations thereof among otherconsiderations.

In certain implementations, the first buffer may comprise one or moresub-buffers. For example, a 128 sample buffer at a sample rate of 25 Hzmay comprise two 64 sample sub-buffers. In another embodiment, acollection of data (i.e., the first buffer which may be 128 samples) maybe divided equally over duration of time, such as for example, 2seconds. For example, a first buffer may be subdivided into 4 equalsub-buffers (which may be, for example, a half second in duration). Inanother embodiment, each sub-buffer may correlate to about half a secondof data, regardless of the size of the buffer. In accordance with oneembodiment, each sub-buffer is independently analyzed from at least oneother sub-buffer (and may be independently buffered from each othersub-buffer in that particular buffer).

Further aspects of this disclosure relate to optionally classifying datathat may be discarded (or otherwise not used in specific analyses)before conducting further analysis (such as for example, FFT analysis).Thus, although certain process, such as block 406 may be implemented tomark, and possibly remove, extraneous data (such as data that isdetermined not to step or arm swing data) with one or more exclusioncriterion, such data may be preserved for later analysis. As oneexample, peaks and/or valleys of accelerometer data may be measured todetermine if they are large enough to be considered walking or running.In certain embodiments, multiple segments of a buffer may be utilized toensure quick arm fluctuations are not misinterpreted by a device, andthus may utilize limited processing power by conducting analysis of thedata, such as for example, entering a frequency analysis mode.

In this regard, certain data may not be used to determine actual stepsbut nonetheless may be used to determine classification of an athleticactivity (e.g., walking, running, playing basketball, etc.) orcalculating energy expenditure, among other determinations (see, e.g.,block 407). In one embodiment, the first buffer may have data indicativeof motion or other physical activity, for example, accelerometer data(alone or in combination with data from one or more other sensors) maycomprise frequencies indicative of detected activity. The activity,however, may not be activity comprising steps. Example embodiments ofclassifying activity and calculating energy expenditure are discussedbelow, including data not utilized to quantify steps, may be found belowin relation to discussions of at least FIGS. 9-12.

Aspects of this disclosure may utilize the sensor data to quantifyactivity, such as a user stepping. In yet other embodiments, steps maybe detected, however, the detected steps may not signify an activity asto which the device or process is configured to detect. For example, adevice (or plurality of devices) may be configured to detect walkingand/or running, but not a shuffling motion commonly performed in asporting environment, such as a basketball game. In this regard,activity within several sports may cause the user to swing their armsand/or bounce, however, are not indicative of walking or running. Forexample, a defensive basketball player often has to shuffle in severaldirections, however, is not walking or running. Aspects of thisdisclosure relate to increasing the accuracy of step counting, andtherefore, may implement processes to remove such movements from stepcounting determinations. In yet other embodiments, however, such datamay be used to determine that the user is performing a specific activityand implement another process based upon this finding. Further, incertain embodiments, even in certain systems and methods for quantifyingstep counts, activities which are considered extraneous to the intendeddetection, however, may be considered in further analysis, such as for adetermination of activity classification.

Regardless of whether block 406 is implemented, systems and methods maybe implemented to quantify steps based upon the data (or a portionthereof). In one embodiment, block 408 may be implemented to process atleast a portion of the data. Analysis (and/or other statisticalmeasures) may be performed on the entire buffer or at least one of thesub-buffers, such as for example, calculating an average (e.g., a meanvalue) and/or a deviation (e.g., variation or standard deviation) ofdata within a sub-buffer. In one implementation, one or more of thefollowing may be performed on the sensor data: scaling, removing forcesof gravity, calculating an absolute value of the data, mean centering ofa value, including raw data and/or the mean-centered absolute value.Those skilled in the art with the benefit of this disclosure willreadily understand that other methods may be implemented to process thedata without departing from the scope of this disclosure.

In accordance with one embodiment, data (such as the data within thebuffer or a sub-buffer) may be compared with a threshold as part ofblock 408 or another process (see, e.g., decision 410). As used herein,discussions relating to a threshold may refer to being lower and/orhigher than a predetermined value or range of values. In one embodiment,vector magnitudes from the sensor data may be calculated. In furtherembodiments, an average value may be determined from the magnitudevectors. As one example, vector magnitude data may be calculated from anaccelerometer signal, which may be utilized to determine an averagevalue of the accelerometer signal. An average value may be calculatedfor every second, 5 seconds, or any duration of time. In one embodiment,the value may be compared with a first threshold for a sub-buffer whichcomprises data within the buffer. In one implementation, if the datawithin the sub-buffer does not meet a threshold, then data within anentire buffer (e.g., the first buffer) may not be utilized in furtherdeterminations of step quantification. Further logic may be utilized todetermine if the sub-buffers have valid data (e.g., data that met thethreshold), and if so, that data is utilized in further step countdeterminations. In one embodiment, contiguous segments (which may be 4sub-buffers) must be assembled that have data (e.g., detectedacceleration) above a threshold to be analyzed (such as, for example, bya frequency determination algorithm). In certain embodiments, the dataof the first buffer (as opposed to the individual sub-buffers) isutilized in further determinations.

If a buffer (e.g., the first buffer or a second buffer, which may be asub-buffer of the first buffer) meets the threshold (and/or passes othercriteria, including but not limited to those described in the precedingparagraphs), that data may be utilized. For example, block 412 may beimplemented to initiate utilizing the data meeting the threshold. FIG.4B provides a flowchart 420 showing an example process for utilizing thedata, which is discussed in the next paragraph. If at decision 410, itwas determined that the data did not meet a threshold, decision 414 maybe implemented. For example, in one embodiment, it may be determined atdecision 410, that mean-centered acceleration data did not meet athreshold value, thereby indicative that there was not enoughacceleration to warrant further processing such as determination of aFFT. In one embodiment, block 412 may be implemented to determine (orretrieve information regarding a determination) whether steps weredetected for the previous sample buffer. If not, the data may bediscarded for purposes of quantifying steps (e.g., block 416). In oneembodiment, an analysis buffer comprising the data may be reset. If,however, the previous sample buffer comprised step data, block 418 maybe implemented to utilize the previous step data in step quantificationsprocesses.

Looking to FIG. 4B, flowchart 420 shows an illustrative example of oneimplementation of processing data that meets the threshold of decision410. Thus, in accordance with one embodiment, flowchart 420 is anexample of one implementation of block 412 and has been labeled as 412a-412 f, respectively, however, those skilled in the art will appreciatethat flowchart 420 may be performed, either in whole or partially,independent of block 412 and/or one or more processes of flowchart 400of FIG. 4A. As indicated in block 412 a, the data is marked or otherwiseplaced into an analysis buffer. In one embodiment, the data includes theaverage magnitude value obtained from half-second duration of activitydata. In one embodiment, non-mean centered data obtained during thecorresponding duration of the acceptable first buffer may be provided tothe analysis buffer. Yet, in another example, derivations calculatedfrom the data meeting the threshold of decision 410 may be placed in theanalysis buffer. In one embodiment, the analysis buffer may be afirst-in last-out (FILO) buffer.

Decision 412 b may be implemented to determine if the analysis buffer ofblock 410 a is full. In one embodiment, the determination may be basedupon or correlated to a duration of activity data. For example, theanalysis buffer may be full upon comprising 5 seconds in duration ofdata for one embodiment. The analysis buffer may be deemed full uponcomprising a quantity of samples. In one embodiment, the analysis buffermay comprise 128 samples. In certain embodiments, the analysis buffermay be larger than the sample buffer described in relation to flowchart400 of FIG. 4A. In one embodiment, the sample buffer may comprise 64samples of data (which may for example correspond to 1 second durationof activity data) and the analysis buffer may comprise 256 samples ofdata (which may correspond to 4 seconds of activity data). The analysisbuffer may comprise the same duration as the first buffer, thus may befull upon obtaining a single sample buffer. Thus, in one embodiment, ananalysis buffer may consist of a single sample buffer. If the analysisbuffer is deemed not full, block 412 may be conducted until the bufferis full.

Upon obtaining a full analysis buffer, decision 412 c may be implementedto classify the data as step data or non-step data. In this regard,certain embodiments may utilize data within the analysis buffer forcalculations of energy expenditure regardless of whether that data isconsidered to comprise step data or a threshold level of step data,however, still may categorize the sensed data into whether a thresholdquantity of steps are detected. In one embodiment, the analysis buffermay be divided into sub-buffers. For example, a 128 sample buffer may bedivided into 4 equal sub-buffers of 32 samples. In another embodiment,the respective sample buffers included as part of the analysis buffermay be utilized in any determinations. Attributes of each sub-buffer orsubsection of data may be utilized. In one embodiment, variance ordeviations between the data may be utilized. For example, the mean andthe standard deviations of each sub-buffer or subsection may becalculated and utilized as part of decision 412 c. The mean of thestandard deviation may be determined in certain embodiments.

In one implementation of block 412 c, activity may be deemed to comprisenon-step data if any of the sub-buffers or sub-sections of data withinthe specific buffer comprise attributes that fail “low thresholdcriteria”. In one embodiment, the low threshold criteria comprises adetermination that a sub-buffer's attribute is less than 50% of the meanof the standard deviation of the other sub-buffers. In one embodiment,the entire analysis buffer may be deemed non-step data, yet in anotherembodiment, only those specific sub-buffers that fail the low thresholdcriteria are deemed to comprise non-step data. Further embodiments mayutilize “high threshold criteria.” In one embodiment, the high thresholdcriteria may comprise a determination whether an attribute of any of thesub-buffers are greater than 180% of the mean of the standard deviationof the other sub-buffers. Like the low threshold criteria, failing tomeet the criteria may result in the entire analysis buffer being deemednon-step data, yet in another embodiments, only those specificsub-buffer that fail the high threshold criteria are deemed to comprisenon-step data.

The low and high threshold criteria may be used in combination such thatboth must be successfully passed, yet in other embodiments, one or morecriteria may be used without implementation or successful completion ofthe other. Failure to pass one or more criteria may result in in notconducting further step-related analysis on at least a portion of thedata, however, data may be utilized for other activity-relateddeterminations (see, e.g. block 412 d). If, however, criteria aresuccessfully met at block 412 c, block 412 e may be implemented toconduct frequency estimation and set up a frequency search range. Infurther embodiments, one or more processes described in relation ofblock 406 may be conducted as part of decision 412 c.

Aspects of this disclosure relate to systems and methods configured toconduct frequency estimation and setting up frequency search ranges tolocate peaks. In one embodiment, peak locating systems and methods maybe utilized on data within a buffer, such as the analysis buffer. Yet inother embodiments, other data may be utilized, alone or in combinationwith data within the analysis buffer. FIG. 5 provides a flowchart 500showing one example process for estimating frequency. Those skilled inthe art will appreciate that FIG. 5 is merely one of many embodimentsthat may be utilized in accordance with various implementations. Lookingto flowchart 500, thresholds for a function to detect frequencies may bedetermined or retrieved (e.g., block 502).

One or more systems or methods for determining identification criteriafor locating peaks may estimate frequency of the data points. Forexample, an average (such as for example, a mean value) and/or astandard deviation (or variance) may be obtained. Such data may beutilized to determine “peaks” and “valleys” (e.g., the high and lowvalues within the data), which may be quantified. Such data may be usedin determinations of dynamic thresholds and/or derivative around thepeak. In one embodiment, weighted averages, such as one or two-passweighted moving averages of data in the buffer may be utilized in anydeterminations. In further embodiments, raw sensor data (e.g.,accelerometer signals) may also be used, either alone or in combinationwith other attributes, such as derivatives of the data.

In one embodiment, a 1-pass weighted moving average, a 2-pass weightedaverage and raw data are each used. In another embodiment, only the2-pass weighted moving average may be used. In one embodiment, the meanand standard deviation of the derivatives are calculated and may be usedas threshold levels. In one embodiment, one or more processes may beutilized to obtain thresholds. For example, a first method may beutilized to locate peaks within a fixed range. Yet in certainembodiments, a second method may be utilized to determine identificationcriteria for locating peaks. In certain implementations, the first,second or additional methods may be implemented based, at least in part,on battery life. For example, the second method may require additionalprocessing power, and therefore, may not be utilized upon receiving anindication that the battery life was decreased below a set point, and/oris declining at a rate above a threshold.

At block 504, a step frequency may be determined for the specificbuffer. In certain embodiments, a mean acceleration of the buffer may beused to create a distinct narrow search range of the data (e.g.,frequencies). The search range may relate the mean acceleration to anexpected walking/running (or other activity) frequency. For example,FIG. 6 shows graph 600 showing the mean acceleration (expressed inmeters per second squared “m/s²”) along x-axis 602 and the frequency inHertz (Hz) of the dual foot step frequency along y-axis 604. Area 606shows the detection area, which may be constrained by boundary lines 608a-608 d. One or more of boundary lines 608 may be based, at least inpart, on the thresholds calculated at block 502. Thus, if accelerationsgenerate a frequency outside the frequency range that the accelerometrypredicts (e.g., outside of boundary lines 608 a-608 d), then certainsystems and methods may not count these as steps. This may be utilizedto ensure data considered to be random noise (e.g. data with differentfrequency content but similar acceleration magnitudes) is not counted asa specific activity (e.g. running). In one embodiment, a mean frequencymay be approximated. In one embodiment, the mean frequency of sensordata measured along one or more axes may be calculated. For example,sensor data collected from one or more accelerometers may be used todetermine the mean frequency along one or more of the x, y and z axes.For example, arm swing data may comprise components along each of thethree axes, and thus measured. In one embodiment, the mean frequency formultiple axes may be approximated by examining the number of peaksand/or valleys in the data.

As shown in FIG. 6, the utilization of boundaries, such as boundaries,608 a-608 d will remove consideration of at least a portion of signalsthat are not likely to be walking and/or running (or another activitybeing selected for). For example, as explained below in relation to FIG.9, signals within the range of 0.5-2.4 Hz (located along y-axis 604) maybe considered as indicative of walking (see, e.g., samples designated by610). In another embodiment, signals within the range of 2.4 to 5 Hz maybe considered as indicative of running. For example, data points 612 mayindicate the athlete is running an 8-minute mile and data point 614 mayindicate that the athlete is running a 5.5 minute mile. Furtherpotential uses for this data as classification of the data as comprising“walking”, “running” or other activity data is discussed below inrelation to FIG. 9; for example, changing activity classification may bedetermined based upon the frequency and the sum of the standarddeviation for 2 portions of data.

In one embodiment, this (and/or other data) may be examined to determinewhether a plurality of consecutive values are within a standarddeviation of the mean. In one embodiment, this analysis may be conductedover a plurality of samples. Further, in certain embodiments, arm swingdata may be utilized to determine the dual foot step frequency (see axis604). For example, if a wrist-worn device is configured to measure armswings, such data may be interpreted as the single foot frequency. Inthis regard, single foot frequency data points designated by element 616may correspond to half the value (with respect to the y-axis 604) todata points 610. In one embodiment, therefore, the value of the singlefoot frequency may be doubled to arrive at the dual foot step frequencyvalue. Those skilled in the art will appreciate that graph 600 is norequired to be generated or displayed, but rather is illustrated hereinto demonstrate aspects of this disclosure.

Decision 506 may be implemented to determine whether to adjust theestimated step frequency. In one embodiment, decision 506 may considerwhether steps were counted (or the frequency of steps) in the previousbuffer. For example, decision 506 may determine whether a successful FFTlocated steps in the previous buffer. As would be appreciated in the artthere may be situations in which the data (e.g., frequencies) change,however, the user may still be conducting the same activity, albeit at adifferent rate or pace. For example, if a user is running at 10 mph andslows to 5 mph, he/she may still running, although at a slower pace. Inthis situation, however, the frequency detected will be altered. Certainembodiments may utilize linear combinations to quantify steps. Forexample, if at block 506, it is determined that previous data indicatedthat the user was walking or running, then in accordance with oneembodiment, the next set of data may utilize this previous data in anydeterminations, such as in a linear combination, such as via block 508.In one embodiment, if there are a first quantity of sections of thebuffer duration that are classified as “running” and a second quantityof section classified as “walking”, systems and methods may be utilizedto determine whether the user has merely adjusted their stride orotherwise changed their speed. In one embodiment, at least a portion ofthe samples within the buffer may be deemed to be within a specificcategory regardless of the data for that portion. For example, ifsamples were collected for 10 intervals and 9 of them were classified asrunning and only a single one was classified as walking, then the entireduration may be deemed running at block 508. In one embodiment, aninterval may only be deemed a different category if it is immediatelypreceded and/or proceeded by data indicative of a consistently differentcategory.

In certain embodiments, an indication that the user is not walking,running or performing another predetermined activity, would prevent orcease utilizing linear combinations of data in step countingdeterminations. For example, this may occur when a user has ceasedstepping (e.g., no longer walking or running). Thus, systems and methodsmay prevent or cease any linear combination processes. In oneembodiment, step quantification may be determined absent linearcombinations, such as for example, by identifying peaks as discussedabove. The estimate (which may have been adjusted via block 508) may beused to establish a search range for bounce peak and/or arm swing peakwithin the data (See, e.g., block 512).

Block 412 f may be implemented to identify a sub-group (or sub-groups)of peaks within the frequency data to utilize in the determinations ofstep quantification. In one embodiment, a FFT is performed and peaks inthe FFT spectrum may be identified, such as with the thresholds and/orderivative around the peak. The performance of the FFT may occur before,during, or after initiating frequency estimation processes, such as oneor more processes described in relation to FIG. 5. In furtherembodiments, the FFT may utilize one or more threshold and derivativesderived from one or more process of flowchart 500. In on embodiment, aspecific peak (or peaks) within the data (such as for example, dataobtained within the first buffer and/or data obtained during first timeframe) may be utilized. This may be conducted based upon determiningthat linear combination cannot be used. In one embodiment, “bouncepeaks,” “arm swing peaks,” and/or other peaks may be identified. Forexample, many users “bounce” upon landing their feet when running. Thisbounce may provide frequency peaks within the data. Other peaks (and/orvalleys) may be present within the sensor data. For example, many usersoften swing their arms in a predictable manner during running and/orwalking to provide “arm swing peaks”. For example, arms usually swingalong an anterior/posterior axis (e.g., front to back). This frequencymay be about half the frequency of the “bounce peaks”. These peaks,however, may each vary independently, based upon, for example, theindividual, the type of motion, the terrain, and/or a combinationthereof.

FIG. 7A shows graph 700 of an example FFT output of sensor data, such asmulti-axis accelerometer data. Graph 700 shows the frequency in Hertz(Hz) along x-axis 702 and FFT power along y-axis 704. Line 706 plots thefrequency (along x-axis 702) against the power (along y-axis 708),wherein the magnitude or maximum height along the y-axis 704 providesthe maximum FFT power for a peak. The peak magnitude indicates therelative strength of the frequency, and may be used as an indicatorwhether a person is stepping. Those skilled in the art will appreciatethat graph 700 is not required to be generated or displayed, but ratheris illustrated herein to demonstrate aspects of this disclosure.

As further seen in FIG. 7A, arm swing range 708 is shown between about 0and 2 Hz along x-axis 702 and comprises arm swing peak 710. Bounce peakrange is shown at about 2-4 Hz along the x-axis 702 and comprises bouncepeak 714. Thus, in the illustrated example, the frequency of the bouncepeak 708 within bounce peak range is generally twice the frequency ofthe arm swing peak. Thus, systems and methods may identify peaks (and/orvalleys) based upon the established thresholds. In this regard,computer-executable instructions of one or more non-transitorycomputer-readable mediums may be executed to determine if a thresholdquantity of peaks are located within the range (either fixed ordynamically determined). If no peaks within the range are located, thatbuffer may be emptied (or otherwise not utilize that data in stepcounting determinations). In this regard, the peaks may refer to thefrequencies may be measured by those with the highest quantity ofoccurrences and/or highest absolute value.

Certain embodiments may determine whether peaks (e.g., arm swing peak,bounce peak, and/or any other peak) meet a threshold. In one embodiment,a threshold of the frequency power within the constrained search rangemay ensure that the frequency is not simply noise and that it is largeenough to be considered an activity (such as, for example, walking orrunning). In yet another embodiment, an overlapping window strategy maybe utilized. For example, FFT windows may be analyzed in an overlappingfashion to make sure short term duration steps are counted. FIG. 7Bshows graph 700 as substantially shown in FIG. 7A, however, furtherincludes arm swing threshold 716 and bounce threshold 718. As shown,peaks within arm swing range 708 (between 0-2 Hz) may only be counted iftheir magnitude meets a threshold of FFT power (e.g., threshold 716 isat about 500 as shown on the y-axis 704).

Likewise, in certain embodiments, peaks within bounce peak range (2-4Hz) may only be counted if their magnitude meets a threshold (suchbounce threshold 718, which is at about 2500 as shown on the on they-axis 704). In certain embodiments, peaks that meet or exceed athreshold may be counted as steps (see, block 412 g). The steps may beincremented for a set time, such as the duration of the FFT analysiswindow. Certain embodiments may continue incrementing with overlappingwindows. In one embodiment, steps may be quantified for each samplebuffer or a certain portion (e.g., 25%) of the analysis buffer and ifthe threshold is met, then steps may be counted for the specific samplebuffer or portion of activity buffer. If, however, the threshold forthat sample buffer or portion is not met, then steps for the remainingportion of the activity buffer (or specific surrounding samples) isdetermined based upon the step frequency. For example, if analysisbuffer comprises 4 sample buffers and only the first 3 have steps, thenthe step count for ¾ of that analysis buffer may be based upon thepreviously selected step frequency.

Further aspects relate to selecting which peaks, if any, are utilized.In accordance with one embodiment, systems and methods may select whichpeaks are to be utilized in quantifying steps despite the fact that thelocated peaks are deemed valid or meet a threshold. As discussed above,bounce data from foot contact may be more reliable arm swing data insome circumstances. Equally, arm swing data may provide more accurateresults in other embodiments. In still further instances, using bothpeaks (and/or others) together to derive a range of data may provide thebest results. Embodiments disclosed herein relate to systems and methodsthat may be used on a portable device configured to be worn on anappendage (such as an arm or leg) to collect activity data and determinewhich peaks to utilize in quantifying steps (and possibly in furtherembodiments, activity type and/or energy expenditure). In this regard,combinations of various peaks may be used to determine specificactivities of the athlete. In certain embodiments, systems and methodsmay be configured to dynamically determine whether to use bounce peaks,such as for example peak 714 or arm swing peaks, such as peak 710. Thedetermination may be updated in substantially real-time (such as every0.5 seconds, 1 second, 2 seconds, 4 seconds, etc.) and based upon theactivity data.

FIG. 8 shows example flowcharts that may be implemented to determinewhether to utilize arm swing frequency or bounce frequency in accordancewith one embodiment. As shown in FIG. 8, the systems and methods may beimplemented to select relevant frequency peaks out of the illustrativeFFT output to determine which data provides the most accurate results(e.g., a which frequency from an FFT analysis of accelerometer datashould be utilized). In certain embodiments, step frequency may be usedin the generation of a step count for the period of time represented bythe FFT spectrum.

In one embodiment, the “relevant” peaks may include arm swing peaks andbounce peaks. Block 801 may be implemented to quantify the number ofidentified peaks within the corresponding search range. Thus, the bouncepeaks located in the frequency estimation for the bounce range (“BR”)(see, e.g., range 708 comprising frequencies between 0-2 Hz of FIG. 7A)may be quantified and the arm swing peaks located in the frequencyestimation for arm swing range (“ASR”) (e.g., range 712 comprisingfrequencies between 2-4 Hz of FIG. 7A) may also be quantified. Incertain embodiments, the quantity of identified peaks (and/or quantityof specific peaks identified) may be utilized to determine which of theestimated step frequencies (e.g., determined by the ASR, BR or peaks inother ranges) may utilized. For example, decision 802 may determinewhether there is at least 1 peak in the BR or at least 1 peak in theASR. If not, block 804 may be implemented to register that no steps wereperformed in the specified range. If, however, there is at least 1 BR orat least 1 ASR peak at decision 802, decision 806 may be implemented todetermine if there is only 1 BR peak (and zero ASR peaks) oralternatively, if there is 1 ASR peak (and zero BR peaks). If it isdetermined that there is only the 1 ASR peak, then block 808 may beimplemented to mark the step frequency at 2*ASR frequency.Alternatively, if it is determined that there is the one BR peak, thenblock 810 may be implemented to mark the step frequency as correspondingto the BR frequency. As a third alternative, if there are more than only1 ASR or only 1 BR in absence of each other, then decision 812 may beimplemented. Before discussing decision 812, it is worth noting to thereader that FIG. 8 (and other flowcharts provided herein) includeseveral decisions, such as for example, decisions 802, 806, 812 and 814.Those skilled in the art with the benefit of this disclosure willreadily appreciate that one or more decisions may be grouped into asingle decision and/or placed in different order, such as incorporatingdecision 804 within decision 802. Thus, the use of a plurality ofdecisions in the current order is merely for illustrative purposes.

One or more processes may determine whether there are exactly 1 BR peakand 1 ASR peak (see, e.g., decision 812). If not, block 824 (which isdiscussed below) may be implemented. If so, however, decision 814 may beimplemented to determine whether the ASR peak is within a set range ofthe BR peak. In one embodiment, decision 814 may determine whether theASR peak is within +/−15% of the ½*BR peak. If so, block 816 may beimplemented to determine that the step frequency is the mean of the BRpeak and 2× the ASR frequency.

If, however, the ASR peak and the BR peak are not within the identifiedrange threshold, then block 818 may be implemented to calculate thedistance from the estimated frequency for each peak. One or moreprocesses may then determine whether the magnitude of at least one ofthe peaks is greater than a threshold. For example, decision 820 may beimplemented to determine if the magnitude of both peaks are greater thana threshold. If the threshold(s) of decision 820 are not satisfied,block 821 may be implemented to select the frequency and magnitude ofthe larger of the two peaks. If the magnitude of the peaks, however, aregreater than a threshold, then step frequency and peak magnitude may bechosen from the peak closer to the estimated step frequency (e.g., block822).

Looking to FIG. 8B showing flowchart 823, systems and methods may beconfigured to determine the step frequency when there are more than 1 BRpeak and more than 1 ASR peak in the search range. In one embodiment,block 824 may be utilized determine the step frequency when there aremore than 1 BR peak and 1 ASR peak in the data. Block 824 may beimplemented upon determining that there is not exactly 1 BR peak and 1ASR peak at decision 812 of FIG. 8A, yet in other embodiments, block 824is independent of decision 812 and/or FIG. 8A. Block 824 may determinepeak proximity to an estimated frequency, such as the frequencyestimated by a frequency estimator (see, e.g., block 412 e and flowchart500). In one embodiment, the BR peak and ASR peak closest the estimatedfrequency are determined. Decision 826 may be implemented to determinewhether at least one identified ASR peak is within a set range of the BRpeak and/or whether at least one identified BR peak within a set rangeof the ASR peak. In one embodiment, decision 826 may determine whetherthe ASR peak is within +/−15% of the ½*BR peak or whether the BR peaksare within +/−15% of ½*ASR peak.

If it's determined at decision 826 that the threshold range set is notmet, then block 828 may be initiated to default to a search range with asingle peak and locate the largest peak in the multi-peak region.Alternatively, block 830 may be implemented if a criterion set forth indecision 826 is satisfied. In one embodiment, if there are multiplepeaks within the set range set forth in decision 826 (e.g., 15%) of thesingle peak range, block 830 may be implemented to select the frequencyand peak magnitude for the biggest peak. Decision 832 may be implementedto determine which of the identified peaks are larger. For example,decision 832 may determine whether the BR peak is larger than the ASRpeak (or vice versa). Decision 832 may merely determine which of the BRpeak and the ASR peak is larger. In one embodiment, the larger of thetwo peaks may be selected as the step frequency (see, e.g., blocks 834and 836).

Further aspects of this disclosure relate to classifying the user'sathletic or physical movements based upon the sensor data. Embodimentsdisclosed herein relate to systems and methods that may be used on aportable device configured to be worn on an appendage (such as an arm orleg) to collect activity data and use the collected activity data todetermine what activity the user is engaging in. FIG. 9 is flowchart 900showing an illustrative example of classifying activity and optionallydetermining speed in accordance with one embodiment. For simplicity, theactivity classifications provided in flowchart 900 are “running”,“walking, and “other,” however, those skilled in the art will appreciatethat other classifications may be implemented in view of thisdisclosure.

Looking to flowchart 900, the activity may be initially classified aswalking or running based upon prior analysis of the sensor data (see,e.g., block 902). This may occur in the same device that collected thesensor data and/or conducted the FFT analysis. In one embodiment, thedevice is configured to be worn on an appendage, such as the user'swrist. In one embodiment, the classification (e.g., as either running orwalking) may be based upon the frequency content of the data. Forexample, the FFT-selected step frequency and time period information maybe utilized to initially classify the activity. The activityclassification determination may be updated in substantially real-time,such as immediately upon completion of the step quantification.

As discussed in relation to FIGS. 6 and 7, data meeting a threshold maybe utilized to determine if the quantified steps are running or walking(or other activities). In certain embodiments, the “signature” of thesignals may be utilized in determining whether the user was walking orrunning (or perhaps, conducting another activity). Signals having acertain range of step frequencies may be indicative of walking, whileothers may be indicative of running. Magnitude data may also be used todetermine activity classifications in certain embodiments. Changingcategories for adjacent data may be based upon the changing“signatures.”

In one embodiment, the analysis buffer may be initially classified as aspecific activity at block 902. Yet, in other embodiments; a separatebuffer may be utilized that has a different duration than the sampleand/or analysis buffer. Although, a classification buffer may have adifferent duration than a first buffer, there is no requirement thatthese (or other) buffers are distinct buffers; rather the second buffermay be a collection of several first buffers and or a logical extensionof other buffers. In this regard, collected data may be stored in asingle location but utilized (even simultaneously for two differentbuffers, processes, and/or analyses).

Further, surrounding data may be used to classify specific sections ofdata. For example, if a previous section data (e.g., at least two datavalues) indicated that the user was walking or running the next set ofdata may utilize the prior data in any determinations, such as in alinear combination. In one embodiment, if there are a first quantity ofsections of the buffer duration that are classified as “running” and asecond quantity of section classified as “walking”, systems and methodsmay be utilized to determine whether the user has merely adjusted theirstride or otherwise changed their speed. In one embodiment, at least aportion of the samples within the buffer may be deemed to be within aspecific category regardless of the data for that portion. For example,if samples were collected for 10 intervals and 9 of them were classifiedas running and only a single one was classified as walking, then theentire duration may be deemed running. In one embodiment, an intervalmay only be deemed a different category if it is immediately precededand/or proceeded by data indicative of a consistently differentcategory.

According to one embodiment, if the activity is initially classified aswalking (e.g., at decision 904), the speed of the walking activity maybe calculated (see, e.g., block 906). Speed may be calculated based uponthe step frequency and/or a feature of the user. For example, in oneembodiment, the speed may be calculated based on the linear combinationof the user's height and the step frequency. Those skilled in the artwill appreciate that other features, including but not limited to, sex,weight, and/or other features may be utilized.

If the initial classification is running, then in one embodiment, finaldetermination of the activity classification (and optionally speed) maybe calculated using the standard deviation (or variance) of each datachunk in the analysis buffer. In one embodiment, speed may be determinedbased upon based upon the step frequency, standard deviation and/or afeature of the user. In one embodiment, the speed may be calculatedbased on the linear combination of the user's height and the standarddeviation of the data chunks in the specified buffer (e.g., the analysisbuffer). Final determinations of the activity classifications may beperformed. In one embodiment, the standard deviation (or variance) forvalues or groups of values in the analysis buffer may be utilized. Forexample, in one embodiment, a plurality of consecutive values (or groupsof values) may be examined to determine whether a threshold level ofconsecutive values is met (See, e.g., decision 910). In oneimplementation, the values may be used to confirm whether there are anumber, such as 3, of consecutive values within one standard deviationof the mean is met for 4 consecutive total values.

Decision 910 may be conducted independently of the result of determiningspeed in block 908. In one embodiment, decision 910 may be conductedduring at least part of the performance of block 908, yet in otherembodiments decision 910 may be performed after block 908 has beeninitiated.

In one embodiment, a negative finding at decision 910 may remove ornegate the “running” classification initially provided to the data atblock 902 (see, e.g., block 912). Further embodiments, however, mayutilize data regardless of whether that data is considered to beindicative of running or walking (or other activity classification). Inone embodiment, data may be utilized in systems and methods fordetermining energy expenditure. Systems and methods for determiningenergy expenditure may utilize this (and other) data to categorize thesensed data into an activity. Such examples are explained later in thisdisclosure.

Returning to decision 910, if the threshold is met (or if therequirement for a threshold is absent), block 914 may be implemented toplace the activity into a classification buffer. The classificationbuffer may be filled with the walking data for which speed wasdetermined (from block 906) as well as the running data from block 908and 910. In certain embodiments, the activity (e.g., walking orrunning), the activity time duration, and calculated speed are placed inthe classification buffer. In one embodiment, an activity buffer may beabout 12.8 seconds in duration. Yet other durations are within the scopeof this disclosure. As discussed above, any buffer (such as theclassification buffer) may have a different duration than another buffer(such as the analysis or sample buffer), however, there is norequirement that these (or other) buffers are distinct buffers; ratherthe second buffer may be a collection of several first buffers and or alogical extension of other buffers. In this regard, collected data maybe stored in a single location but utilized (even simultaneously for twodifferent buffers, processes, and/or analyses).

Decision 916 may be implemented to determine if the classificationbuffer of block 914 is full. In one embodiment, the determination may bebased upon or correlated to a duration of activity data, such as 12.8seconds in one embodiment. The analysis buffer may be deemed full uponcomprising a quantity of samples. In one embodiment, the analysis buffermay comprise 12 samples.

As discussed above, flowchart 900 provides one of many embodiments thatmay be executed in accordance with this disclosure. For example, system100 may process data received from one or more of the sensors describedabove to attempt to classify a user's activity. For example, system 100may compare a sensor signal to one or more signal or activity“templates” or “signatures” corresponding to selected activities. Incertain embodiments, templates may be created by attaching sensors to auser and monitoring signals generated when the user performs variousactivities. In accordance with certain embodiments, an activity may beassociated with an activity template specific to user 124. In one suchembodiment, user 124 may be assigned a default template for a specificactivity unless a specific template has been assigned to that activity.Thus, user 124 may create or receive (but is not required to create orreceive) an activity template that may be more accurate than a defaulttemplate because the template is more specific to the user and/or theactivity. User 124 may have the option to create templates for one ormore predefined or undefined activities. A specific or otherwise newtemplate might be shared among the community of users. Shared templatesmay be based on a variety of different sensors. In some embodimentstemplates may be refined or adjusted for use with different sensors. Forexample, a template that was created for use with a shoe based sensormay be refined for use with a wrist-worn sensor.

An activity template may be created from data obtained from one or moreof a plurality of different sensors. For example, a first group ofsensors (e.g. sensors 126 and 138) may be utilized in the formation orrefinement of a first activity template; however, a second group ofsensors (e.g., sensors 128 and 140) may be utilized in the formation orrefinement of a second activity template. In yet further embodiments, athird group of sensors, such as sensors 128 and 140 (and/or othersensors), may be utilized in the creation of the first activity templatefor a second user (e.g., not user 124) than utilized for the formationof the same activity template as user 124. Thus, in accordance withcertain embodiments, there is no requirement that data from a specificsensor be received for either: 1) the same activity template fordifferent users; and/or 2) different activity templates for the sameuser.

In one embodiment, a wrist mounted accelerometer, which may be amulti-axis accelerometer, may be attached to a user and signal templatesbased on the accelerometer output when the user runs, walks, etc. may becreated. The templates may be functions of the sensor(s) used and/or thelocations of the sensor(s). In some embodiments, a single signal (orvalue) is created by combining multiple signals (or values). Forexample, three outputs of a three axis accelerometer may be summed orotherwise combined to create one or more signals. Example step 902 mayinclude comparing a signal, multiple signals or a combination of signalsto one or more templates. In some embodiments, a best match approach maybe implemented in which every activity is attempted to be classified. Inother embodiments, if a signal, multiple signals or combination ofsignals does not sufficiently match a template, the activity may remainunclassified.

FIG. 10 shows a flowchart 1000 of yet another embodiment for estimatingenergy expenditure. In one embodiment, motion data from one or moresensors may be obtained. (see, e.g., block 1002). In certainimplementations, sensor data from only a single device configured to beattached to a human appendage may be obtained. In one embodiment, sensordata from a single accelerometer (single axis or multiple axis) may beused alone. In one embodiment, raw sensor data may be used. In oneembodiment, raw acceleration data may be obtained. The sensor data maybe processed to remove the effects of gravity. In one embodiment, theEuclidean norm of the sensor data (raw or processed) may be calculated.The data may comprise of consist of accelerometer data. Accelerometerdata may be obtained at 25 Hz. In certain embodiments, a sample buffercomprising 25 samples is obtained. In one embodiment, each samplerepresents about 0.5 seconds of activity data.

Block 1004 may be implemented to classify the data (or attempt toclassify data) into an activity. This may occur once a buffer, such as asample buffer, is full. For example, obtaining 25 samples in a 25 samplebuffer. The classification of activity may include one or more of theclassification systems or methods described herein, including one ormore aspects described in relation to FIG. 9. In one embodiment, Fouriertransform step algorithm may be implemented, such as describedpreviously in this disclosure. Of course, the data may already beclassified or readily classified based upon previous classification ofderivative data. Thus, in one embodiment, corresponding activityclassifications may already be known for at least a portion of the data.In certain embodiments, activity may be classified as walking, jogging,running, (or unclassified). In another embodiment, data may beclassified as either walking or running (or deemed unclassified).

An energy expenditure value may be determined for the classifiedactivity (see, e.g., block 1006). The determination of the energyexpenditure value may utilize one or more data points comprising theuser's personal information, such as for example, age, weight, sex,height, and combinations thereof. Yet in other embodiments, some or allof any known personal information may not be utilized. In oneembodiment, the user information may be stored on a non-transitorycomputer-readable medium located on the device comprising a sensor thatsensed activity data utilized in the analysis. In one embodiment, theuser information is obtained entirely from a device configured to beworn on a human appendage that comprises at least one sensor. In oneembodiment, the device contains all of the sensors and user informationutilized in the determination.

In yet other embodiments, however, system and methods could calculateenergy expenditure values without at least one type of personalinformation based upon whether the data is obtained from a first sensor(or first type of sensor). In other embodiments, at least one type ofpersonal information may be utilized if data is obtained from a secondsensor (or type of sensor). Sensors or devices may be identifiable froma unique identifier, such as for example, a serial number, MAC address,or the like. Yet in other embodiments, a sensor or device may beidentifiable from a non-unique identifier, such as for example, a modelnumber from a device with a sensor. In further embodiments, a defaultvalue may be obtained or derived. In certain embodiments, a defaultvalue may be intentionally discounted due to variations from device todevice.

Systems and methods may be implemented to assign an energy expenditurevalue for unclassified data (see, e.g., block 1008). In one embodiment,a Euclidean mean value may be calculated based upon the data. Forexample, the average acceleration of the Euclidean norm may becalculated. In one embodiment, if the entire (or substantially theentire) duration of activity data reflects that he activity wasconsistent, such as for example, 1 second intervals within the durationindicated the user was walking or conducting a consistent activity, thena first default process may be utilized to determining activity. Datafrom one or more sensors may be utilized. In one such embodiment, datafrom several accelerometers (and/or a multi-axis accelerometer) may benormalized to generate a value. Values (which may be normalized) may beplaced into a buffer. In one embodiment, a sample buffer may beutilized. The sample buffer may be a 1-second buffer. In certainembodiments, a variable sampling rate may be utilized. In one suchembodiment, 25 samples may be obtained in 1 second. In yet otherembodiments, other rates may be utilized, including for example, a fixedrate. In one embodiment, data from several accelerometers (and/or amulti-axis accelerometer) captured at an interval (e.g., 1 second) maybe summed and the average of the absolute value of the acceleration maybe calculated. A default energy expenditure value may be assigned basedupon the acceleration count. An equivalence value, such as a MetabolicEquivalence Value (MET value) may be determined from the data within thebuffer. In one embodiment, a rectangular hyperbola process may beutilized in determinations of an equivalence value.

In certain embodiments, the determination of the energy expenditurevalue may utilize one or more data points comprising the user's personalinformation, such as for example, age, weight, sex, height, restingmetabolic rate (RMR) and combinations thereof. Yet in other embodiments,some or all of any known personal information may not be utilized. Inone embodiment, the user information may be stored on a non-transitorycomputer-readable medium located on the device comprising a sensor thatsensed activity data utilized in the analysis. In one embodiment, theuser information is obtained entirely from a device configured to beworn on a human appendage that comprises at least one sensor. In oneembodiment, the device contains all of the sensors and user informationutilized in the determination. The energy expenditure value may bedetermined for each 1 second interval of data based on the user's RMRand a MET value.

In certain embodiments, the energy expenditure of the classified and/orunclassified energy expenditure values may be accumulated (see, e.g.,block 1010). Energy expenditure values for both the classified and theunclassified activities may be accumulated, and in one embodiment,caloric burn may be determined using this and/or other information. Inaccordance with one implementation, the energy expenditure values forthe classified activity may be placed in a buffer, which in oneembodiment may be a buffer having a time frame that is larger than anactivity buffer or another buffer. (see, e.g., sub-block 1010 a). This,in certain embodiments, may ensure that the information, such as thatfrom the 12.8 activity buffer (See, block 910), is not double counted.Sub-block 1010 b may be adjusted by subtracting the previous 12.8 secondand adding the energy expenditure (e.g. caloric burn) from theclassified activity. Those skilled in the art will appreciate that 13second and 12.8 seconds are merely examples. In certain embodiment, thebuffer may be a first in-first out (FIFO) buffer. The total energyexpenditure points for both the classified and the unclassifiedactivities may then be totaled for the respective time period and one ormore buffers (such as the sample, energy expenditure, and/or activitybuffer(s)) may be reset.

FIG. 11 shows a visual depiction of an example correlation of activityto energy expenditure determinations. In one embodiment, the depictionshows an example correlation of acceleration measurements that is notclassified (such as part of the flowchart shown as FIG. 10) correlatedto energy expenditure values. Yet in other embodiments, at least aportion of classified data may be correlated through this or similarprocesses. The top portion of FIG. 11 includes graph 1100 that plotsmeasured acceleration (see, y-axis 1102) over time (see, x-axis 1104).In one embodiment, the acceleration may be analyzed as the Euclideannormal of multiple axis (such as the x, y and z axes) over time. Datasection 1106 shows a collection of data (namely between 0.3 to about 0.5on the x-axis 1104), in which the acceleration values are consistentlyelevated when compared to other values on graph 1100, such as theacceleration values of data section 1108 which correspond to around 0.9on the x-axis 1104. Specifically, the acceleration data values of datasection 1108 are around zero (0). As seen in graph 1110, located on thelower portion of FIG. 11, the cumulative energy expenditure (as measuredin calories along the y-axis (1112) along the same time scale (see,scale 1114 being at about the same scale as x-axis 1104). As furtherseen in graph 1110, the corresponding energy expenditure is correlatedto the acceleration values set forth in graph 1100. Thus, thecorresponding accumulation at about location 1116 (which corresponds todata section 1106) is much higher than the corresponding accumulation ofenergy expenditure values at about location 1118, which corresponds todata section 1108). In fact, location 1118 shows little or no increasein energy accumulation values.

III. Energy Expenditure Point Calculations

In some embodiments there is not a one-to-one correlation between anactivity and an activity factor. The selection of an activity factor maybe based on several different variables, such as the activityidentified, steps taken, heart rate, and intensity of a workout. Theactual activity identified may correspond to a group of activity factorsand the other variables may be used to make a final selection of anactivity factor. In still other embodiments there is a one-to-onecorrespondence between an activity and an activity factor. In some ofthese embodiments, other variables such as steps taken, heart rate, andintensity of a workout may be used to adjust or compensate for theactivity factor. Of course, in some embodiments there is a one-to-onecorrelation between activities and activity factors and no adjustmentsor compensations are made to the activity factor.

FIG. 12 illustrates a method for calculating energy expenditure values,such as points, in accordance with an embodiment of the invention. Afterat least one of user's 124 activity is classified (see, e.g., block1204, which may use for example one or more classification systems andmethods disclosed herein), block 1204 may be implemented to determine acorresponding activity factor. An activity factor may correspond tobrisk running, running at a moderate pace, walking slowly or any otheractivity. An activity factor for an activity may be related to caloriesor energy generally required to perform the activity. If an activity wasnot classified in step 1202, a default activity factor may be selectedor derived. In some embodiments multiple default activity factors may beutilized. An activity's intensity, duration or other characteristic(s)may be assessed, from which one or more default activity factors may beapplied. The plural activity factors may be set via medians/averages,ranges, or other statistical approaches.

In various embodiments of the invention, activity factors are used tocalculate energy expenditure points. After at least one of user's 124activity is classified, in step 1206 energy expenditure points (“EEP”)may be calculated. The use of energy expenditure points allows forcomparison of activity levels and may promote collaboration among users,normalize for competition among users of different capabilities, andotherwise encourage activity. In one embodiment, energy expenditurepoints are calculated as follows:EEPs=AF*duration  (Equation 1)Wherein:

-   EEPs=energy expenditure points-   AF=activity factor determined in step 1204-   duration=duration of the activity classified in step 1202

Step 1206 may be performed at a device that includes sensors thatmonitor activity and/or at another device that includes a processor,such as a mobile phone (see, e.g., 138) or server (see, e.g., 134). Inyet other embodiments, block 1206 may be performed at a deviceconfigured to be worn on a human appendage (e.g., wrist, arm, neck,ankles, leg, etc.). The device may be the same device comprising sensorsutilized to collect the activity data. In one embodiment, the samedevice comprises sensors that collect all of the sensor data and/orotherwise contains all the information locally to compute the activitydata.

In some embodiments, Equation 1 may be modified to include a scalar thatis multiplied by the activity factor and duration. The scalar may beselected so that typical energy expenditure points fall within a desiredrange. The range of points may be desired for various games orcompetitions.

Variations of Equation 1 may be used in other embodiments of theinvention. In some embodiments, users may select an equation and/or oneor more variables, such as for example, a scalar. Equations may beselected for different games and competitions. In one example a groupmay set handicaps among the players based on fitness, so that the mostfit generate EEPs only if they do a common activity or set of activitiesfor longer period(s) of time. A group of users participating in anenergy expenditure point competition may agree on a particular equationor method before beginning the competition. In some embodiments of theinvention, a user may participate in multiple competitions and earndifferent points for the same activity because of different calculationmethods. For example, a user may be participating in two competitionsthat have unique calculation methods. The user may earn two differentpoint totals for the two different games and a third point total foetheir overall energy expenditure. Some point totals may be maintainedseparate from an overall point total.

After the energy expenditure points are calculated, the calculatedpoints may be combined, such as being added, to a total in block 1208.The total may allow user 124 (and/or selected individuals or groupsapproved by user 124) to see how many points are earned over variousperiods of time, such as days, weeks and months. Totals may also becalculated for multiple time periods. For example, a user may receivetotals for periods that include 24 hours, one week, one month and oneyear. In some embodiments users may select other time periods ordeselect time periods. A user may track multiple time periodsconcurrently and track points award since the beginning of use of adevice or start of a program. The total for any giving time period mayrepresent points earned for several activities. For example, in a day auser may receive points for walking, jogging and sprinting duringdifferent time periods. As mentioned above, the points earned for eachactivity may be a function of a corresponding activity factor.

Energy expenditure points may be deducted when user 124 has beeninactive for a predetermined period of time or enhanced when certaincriteria are met. This feature may be included with all calculations ormay be used in various games and competitions. For example, in step 1214it may be determined whether an adjustment criterion has been met. Theadjustment criteria may include inactivity for a predetermined timeperiod. In some embodiments inactivity is not determined by merelydetermining that an amount of time has passed since with user wasactive.

When an adjustment criterion has been met, the total of energyexpenditure points may be adjusted in step 1210. The adjustment may be afunction of duration of inactivity. In some embodiments, a device maywarn user 124 (or authorized groups/individuals) that they are close toreceiving a reduction in energy expenditure points to encourageactivity. It yet other embodiments, an alarm may notify user 124 (and/orother authorized individuals and/or groups) that they have received areduction of energy expenditure points. In certain embodiments,team-mates and/or competing users may be notified of a reduction (orpotential for reduction). In further embodiments, teachers, trainers,and/or parents may more readily monitor the physical activity of others.When a user has not been inactive, the process may end in step 1214. Ofcourse, the method shown in FIG. 12 may be repeated at various intervalsand allow for tracking points concurrently for different time periods,such as days, weeks and years.

In another aspect, a device 10, such as device 226 may provide a messagebased on inactivity or non-active periods. If the device senses that theuser has been in a non-active (e.g., low activity) state for apredetermined amount of time, an alert message may be delivered to theindicator system or display to remind the user to become more active.The alert message can be delivered in any of the manners describedherein. The threshold levels of a low activity state and amount ofinactive time could also vary and be individually set by the user.

In some arrangements, user non-activity or inactivity may also bedetected and affect the user's progress toward completion of an activitygoal. For example, inactivity may be detected when a user does notexhibit movement of a particular level or a type of movement for aspecified amount of time, does not exhibit a heart rate of at least athreshold level, does not move a sufficient amount of distance over anamount of time and the like and/or combinations thereof. Forarrangements in which a user accumulates activity points to reach anactivity point goal, points or a value may be deducted from the user'sactivity point or other activity metric total when an amount ofnon-activity (e.g., inactivity or sedentary state) is detected. Variousconversion rates for converting inactivity to activity point deductionsmay be used. In a particular example, 10 minutes of inactivity maycorrespond to a 5 point deduction. In another example, 30 minutes ofinactivity may correspond to a 100 point deduction. Loss or deduction ofactivity points may be linear or may be non-linear, for example,exponential, parabolic and the like.

A user's non-active time may include inactive time and sedentary time.Inactivity and sedentary time may be defined by different movement,heart-rate, step or other thresholds or may be defined using the samethresholds. In one example, sedentary time may have a higher threshold(e.g., requiring a higher level of activity) than an inactivitythreshold. That is, an individual may be considered sedentary but notinactive. The non-active threshold may correspond to the sedentarythreshold or a higher threshold, if desired. Alternatively, aninactivity threshold may be greater than a sedentary threshold. Theremay also be multiple sedentary thresholds, inactivity thresholds and/ornon-active thresholds (e.g., each of the sedentary and inactivitythresholds may be a non-active threshold). Different point deductions orrates of point deductions may also be defined between the multiplethresholds and levels of little to no activity (e.g., non-activity). Forexample, a user may lose 50 points per hour for inactivity and 30 pointsper hour for sedentary activity or vice versa. Further, activity pointdeduction may be triggered at different times depending on if the useris inactive or sedentary. For instance, a user may begin losing activitypoints after 30 minutes of inactivity or 45 minutes of being sedentary.Additional thresholds (e.g., more than two thresholds) and correspondingrates of activity point loss may also be defined.

In some arrangements, various sensors may be used to detect non-activeperiods of time. As discussed, non-activity time periods may be definedbased on heart-rate, amplitude of a movement signal, step rate (e.g.,<10 steps per minute), or the like. Alternatively or additionally,inactivity and sedentary time periods may be measured based on aphysical position, body position, body orientation, body posture of ortype of activity being performed by the individual. The detrimentaleffects of various physical inactivity or sedentary body positions ororientations may also differ. Accordingly, 30 minutes of reclining mayintroduce the same health risks as 45 minutes of sitting. The potentialfor health risks may also be time-dependent. Accordingly, non-activity(e.g., sleeping) for a specified range of durations and during aspecified range of time might not introduce health risks. In oneexample, sleeping for 7-9 hours between 9 PM and 9 AM might notintroduce detrimental health risks and thus, might not contribute toactivity point or other activity metric value deduction. Indeed, in someexample, a lack of inactivity (such as sleep) for a specified range ofdurations and/or during a specified range of time may be considereddetrimental to a user's health. Thus, activity points may be deducted oractivity points may be accumulated at a slower rate during these times.

Alternatively or additionally, the amount by which a value of theactivity metric (e.g., an activity points) is decreased may bedetermined based on time of day, location of the user, physical positionof the user, level of inactivity and the like. For example, a user maylose greater value in an activity metric and/or at a faster rate duringthe afternoon than during the evenings. In another example, if a user isat a gym, the user may lose fewer activity points or other activitymetric or lose value in the metric at a slower rate than if the user waslocated at home.

To account for the variances in types of non-active activity (e.g.,below a requisite level of movement to be considered activity), a systemmay distinguish between physical body positions or orientationsincluding, for example, sleeping, reclining, sitting and standing.Distinguishing between different physical body positions andorientations may include placing sensors at different locations of theuser's body to detect the individual positions of each body part. Thephysical body position of the user may then be determined based on therelative positions of the body parts to one another. For example, when aknee location sensor is within a first threshold distance of a waist orchest sensor, the system may determine that the user is sitting. If theknee location sensor is outside of the first threshold distance, thesystem may determine that the user is standing. In the above example,the system may use a portion of the distance such as the verticaldistance. By using vertical distance alone or in combination with anabsolute distance (e.g., straight line distance between the twosensors), the system may further distinguish between when a user islying down and standing up. For example, a lying down position maycorrespond to a very low vertical distance between the knee sensor andchest or waist sensor even though the absolute distance may be larger. Astanding position may correspond to a larger vertical distance betweenthe knee sensor and the waist or chest sensor but exhibit a similarabsolute distance. In other examples, an angle formed by the varioussensors may be used to determine an individual's position. Additionallyor alternatively, the location of the user's various body parts may beevaluated in conjunction with accelerometer or movement data todetermine if the user is exhibiting movement or (e.g., at, above orbelow) a specified level of movement.

In addition to deductions in activity points, the system may alert auser to inactivity to encourage active lifestyles. In one example, thesystem may alert the user by displaying a message or indicator on adevice such as the wearable device assembly described herein after aspecified amount of inactivity such as 2 minutes, 5 minutes, 30 minutes,1 hour and the like. The amount of inactivity time may be additive overnon-consecutive time periods. An amount of consecutive inactivity timemay alternatively or additionally be tracked. For example, if the useris inactive between 10:15 and 11:00 AM and then again between 2:00 and2:30 PM, the total amount of non-active time may be 1 hour and 15minutes. The message or indicator of inactivity may be provided as awarning prior to deducting activity points. For example, the message mayindicate that X amount of activity points will be deducted if the userdoes not exhibit a sufficient level of activity within a specifiedamount of time (e.g., 30 minutes, 5 minutes, 10 seconds, 30 seconds, 1hour, 2 hours, etc.). Accordingly, the device may include a non-activetimer to determine the amount of user non-activity. Additionally, themessage may provide a suggestion as to a type of activity the usershould perform to counter any risks introduced by the inactivity. Forexample, the system may suggest that the user walk 1 hour at a 10 minutemile pace. When the user has counteracted or accounted for the risks ornegative effects of the detected amount of inactivity time, acelebratory message or other indication may be provided.

Warnings, point deductions and/or other notifications may be provided ifa user returns to a sedentary or a non-active mode within a specifiedamount of time of exiting sedentary or a non-active mode. For example,the user may exercise or exhibit a sufficient level of activity to exitthe sedentary or a non-active mode for a period of 10 minutes. However,the system or device may require at least 30 minutes of activity toavoid additional warnings for a period of time such as 1 hour, 2 hours,3 hours, etc. For example, the warnings may indicate that the user didnot exhibit activity for a sufficient amount of time or a sufficientlevel of activity or a combination thereof. Additionally, multiplesedentary periods within short amounts of time (e.g., a threshold amountof time) may require higher or additional levels of activity tocounteract potential sedentary effects including health risks and thelike. In a particular example, the user may be required to perform ahigher level of activity to halt point deduction.

The device or other system may further advise a user as to an amount ofnon-active time allowed before negative health effects may occur. In oneexample, the device or system may include a countdown indicating aremaining amount of allowable non-active time before potential healthrisks may begin taking effect. An amount of permissible non-active timemay be earned or accumulated based on an amount of activity performed.Accordingly, the device may also provide suggestions or recommendationsas to a type and/or duration of activity that may be performed to earn aspecified amount of non-active time (e.g., 1 hour of TV watching).Different types of non-active or sedentary activities may requiredifferent types or amounts of activity. For example, 1 hour of recliningmay require more strenuous or longer exercise than 1 hour of sitting. Inanother example, 1 hour of sitting while knitting may require lessstrenuous or a lower amount of exercise or activity than 1 hour ofsitting while watching television. According to one or morearrangements, recommendations may be generated based on empirical dataand/or predefined programming and data tables specifying a type and/orduration of activity and a corresponding amount of permissiblenon-activity.

The device or activity tracking system may further recommend activitiesbased on historical records. For instance, the device or tracking systemmay determine activity performed by the user in the past and generaterecommendations based on those types of activities. Additionally oralternatively, the device or tracking system may generaterecommendations for specific workouts performed by the user in the past.For example, a user may need to perform 500 calories worth of activityto counteract 2 hours of TV watching. In such a case, the system mayrecommend a particular workout performed by the user in the past inwhich the user burned 500 calories. Combinations of historical activitytypes and specific historical workouts may be used to generaterecommendations. In one example, the system may recommend one of twoworkouts that the user has performed in the past based on a type ofworkout that the user appears to prefer. The preference may bedetermined based on a number of times the user has performed each typeof workout. A workout or activity type may also be recommended based onlocation and time. For example, if a user previously performs aparticular type of activity or a particular workout routine at the samelocation and/or at the same time, the system may recommend that type ofactivity or workout routine. Other recommendations algorithms andfactors may be used.

System 100 may be configured to transmit energy expenditure points to asocial networking website. The users may be ranked based on their totalnumber of points for a desired time interval (e.g., rank by day, week,month, year, etc.).

CONCLUSION

Providing an activity environment having one or more of the featuresdescribed herein may provide a user with an experience that willencourage and motivate the user to engage in athletic activities andimprove his or her fitness. Users may further communicate through socialcommunities and challenge one another to participate in pointchallenges.

Aspects of the embodiments have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one of ordinary skill in the art willappreciate that the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of theembodiments.

What is claimed is:
 1. A unitary apparatus comprising: a unitary housingconfigured to be worn on an appendage of a user, comprising: aprocessor; a sensor configured to capture motion data of the user; anon-transitory computer-readable medium comprising computer-executableinstructions that when executed by the processor perform at least:capturing motion data of the user with the sensor while being worn on anappendage of the user; adding the motion data into one or more buffers;detecting arm swings peaks and bounce peaks in the motion data;determining whether to utilize the arm swing peaks or the bounce peaksin the motion data to quantify steps; and calculating a step frequencyof the user during a time period based on at least one of the utilizedarm swing peaks or bounce peaks in the data; classifying the motion dataas running or walking based upon the calculated step frequency duringthe time period; calculating an energy expenditure value for the user,based on the classified motion data; calculating an energy expenditurepoints value, based on the calculated energy expenditure value; andadjusting the energy expenditure points value, based on a duration ofinactivity of the user.
 2. The unitary apparatus of claim 1, wherein thenon-transitory computer-readable medium of the unitary apparatuscomprises further instructions that when executed by the processor,perform at least: receiving a metabolic equivalence value correspondingto the classified data from the computer-readable medium on the unitaryapparatus, wherein the metabolic equivalence value is utilized tocalculate the energy expenditure value.
 3. The unitary apparatus ofclaim 2, wherein the non-transitory computer-readable medium of theunitary apparatus comprises further instructions that when executed bythe processor, perform at least: determining that at least a portion ofthe motion data cannot be categorized as either running or walking, andin response, conducting an energy expenditure determination that assignsa metabolic equivalence value to the uncategorized motion data.
 4. Theunitary apparatus of claim 3, wherein the calculation of the energyexpenditure value comprises combining energy expenditure values ofclassified activities and energy expenditure values of unclassifiedactivities.
 5. The unitary apparatus of claim 2, wherein the time periodis a first time period, and further comprising a display configured tobe observable by the user while being worn by the user, and thenon-transitory computer-readable medium of the unitary apparatuscomprises further instructions that when executed by the processor,perform at least: combining the energy expenditure value for the firsttime period with an energy expenditure value from a second time periodto determine an accumulated energy expenditure value; and displaying theaccumulated energy expenditure value on the display of the unitaryapparatus.
 6. The unitary apparatus of claim 1, wherein the sensorcomprises an accelerometer, and the non-transitory computer-readablemedium of the unitary apparatus comprises further instructions that whenexecuted by the processor, perform at least: determining accelerometermagnitude vectors from the accelerometer for a time frame; calculatingan average value from magnitude vectors for the time frame; anddetermining whether the magnitude vectors for the time frame meet anacceleration threshold and be used to quantify steps for at least thetime frame.
 7. The unitary apparatus of claim 6, wherein thenon-transitory computer-readable medium of the unitary apparatuscomprises further instructions that when executed by the processor,perform at least: determining that the magnitude vectors for the timeframe did not meet an acceleration threshold and therefore are not usedto quantify steps for at least the time frame; and utilizing the datathat did not meet the acceleration threshold in a calculation of anenergy expenditure value.
 8. The unitary apparatus of claim 7, whereinthe non-transitory computer-readable medium of the unitary apparatuscomprises further instructions that when executed by the processor,perform at least: determining that at least a portion of the data meetsthe acceleration threshold and in response, placing acceleration datawithin an analysis buffer; calculating a mean acceleration value of theanalysis buffer to create a search range of acceleration frequenciesrelated to an expected activity; analyzing frequencies of theacceleration data within the search range to identify at least onebounce peak and one arm swing peak; and determining whether to utilizeat least one of the bounce peak and the arm swing peak to quantifysteps.
 9. The unitary apparatus of claim 8, wherein the search rangecomprises an arm swing range and a bounce range, and wherein analyzingfrequencies within the acceleration data comprises: identifying a firstfrequency peak as an arm swing peak if the first frequency peak iswithin the arm swing range and meets an arm swing peak threshold; andidentifying a second frequency peak as a bounce peak if the secondfrequency peak is within the bounce range and meets a bounce peakthreshold.
 10. The unitary apparatus of claim 9, wherein the determiningwhether to utilize the bounce peak or the arm swing peak to quantifysteps comprises: quantifying a number of arm swing peaks and bouncepeaks; and utilizing the quantification of arm swing peaks and bouncepeaks in a calculation to choose a step frequency and step magnitude.11. The unitary apparatus of claim 10, wherein the non-transitorycomputer-readable medium of the unitary apparatus comprises furtherinstructions that when executed by the processor, perform at least:based upon the chosen frequency and step magnitude, quantifying a numberof steps taken by the user during a respective time frame; and basedupon the number of steps taken, classifying the user's motion as runningor walking for the respective time frame.
 12. The unitary apparatus ofclaim 11, wherein the time period is a first time period, and thenon-transitory computer-readable medium of the unitary apparatuscomprises further instructions that when executed by the processor,perform at least: based on the classified user's motion, assigning anenergy expenditure value for the first time period; combining the energyexpenditure value for the first time period with an energy expenditurevalue from a second time period to calculate an accumulated energyexpenditure value; and displaying the accumulated energy expenditurevalue on the display of the unitary apparatus.
 13. The unitary apparatusof claim 12, wherein the energy expenditure value from the second timeperiod comprises data that is not classified into an activity.
 14. Theunitary apparatus of claim 12, wherein the non-transitorycomputer-readable medium of the unitary apparatus comprises furtherinstructions that when executed by the processor, perform at least:receiving a user input from a user input device located on the userinput device, and in response, displaying the energy expenditure valueon the display.
 15. A non-transitory computer-readable medium comprisingcomputer-executable instructions that when executed by a processorperform at least: capturing motion data of a user with a sensor worn onan appendage of the user; quantifying steps taken by the user,comprising: detecting arm swing instances and bounce instances in themotion data from the sensor worn on the appendage; determining whetherto utilize the arm swing instances or the bounce instances in the motiondata to quantify the steps; and using only motion data collected fromthe sensor worn on the appendage, calculating a step frequency of theuser during a time period based on at least one of the utilized armswing instances or bounce instances in the data; classifying the motiondata as running or walking based upon the calculated step frequencyduring the time period; calculating an energy expenditure value for theuser, based on the classified motion data; calculating an energyexpenditure points value, based on the calculated energy expenditurevalue; and adjusting the energy expenditure points value, based on aduration of inactivity of the user.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the computer-executableinstructions, when executed by the processor, are further configured toperform at least: generate a warning message for the user prior toadjust the energy expenditure points value.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the time period is a firsttime period, and wherein the calculated energy expenditure value is afirst energy expenditure value and the computer-readable medium furthercomprising instructions that when executed by the processor, perform atleast: combining the energy expenditure value for the first time periodwith an energy expenditure value from a second time period to determinean accumulated energy expenditure value; and displaying the accumulatedenergy expenditure value on a display of a device configured to be wornby the user during collection of the motion data.
 18. The non-transitorycomputer-readable medium of claim 17, wherein captured motion data ofthe user is only from one or more sensors that are located on thedevice.
 19. The non-transitory computer-readable medium of claim 18,wherein all information used to calculate the energy expenditure valueis either (a) located on the device before collection of the motion dataor (b) derived from the motion data without information external to thedevice.
 20. The non-transitory computer-readable medium of claim 19,wherein the sensor comprises an accelerometer.